ORCID Profile
0000-0002-3399-2291
Current Organisation
Khalifa University of Science and Technology
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Publisher: MDPI AG
Date: 08-10-2016
DOI: 10.3390/S16101637
Publisher: IEEE
Date: 06-2015
Publisher: Unpublished
Date: 2014
Publisher: Unpublished
Date: 2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2019
Publisher: Wiley
Date: 14-06-2015
Publisher: IEEE
Date: 05-2014
Publisher: Elsevier BV
Date: 12-2013
Publisher: Inderscience Publishers
Date: 2019
Publisher: MDPI AG
Date: 28-10-2020
Abstract: Recent evolutions of the Unmanned Aircraft Systems (UAS) Traffic Management (UTM) concept are driving the introduction of new airspace structures and classifications, which must be suitable for low-altitude airspace and provide the required level of safety and flexibility, particularly in dense urban and suburban areas. Therefore, airspace classifications and structures need to evolve based on appropriate performance metrics, while new models and tools are needed to address UTM operational requirements, with an increasing focus on the coexistence of manned and unmanned Urban Air Mobility (UAM) vehicles and associated Communication, Navigation and Surveillance (CNS) infrastructure. This paper presents a novel airspace model for UTM adopting Performance-Based Operation (PBO) criteria, and specifically addressing urban airspace requirements. In particular, a novel airspace discretisation methodology is introduced, which allows dynamic management of airspace resources based on navigation and surveillance performance. Additionally, an airspace sectorisation methodology is developed balancing the trade-off between communication overhead and computational complexity of trajectory planning and re-planning. Two simulation case studies are conducted: over the skyline and below the skyline in Melbourne central business district, utilising Global Navigation Satellite Systems (GNSS) and Automatic Dependent Surveillance-Broadcast (ADS-B). The results confirm that the proposed airspace sectorisation methodology promotes operational safety and efficiency and enhances the UTM operators’ situational awareness under dense traffic conditions introducing a new effective 3D airspace visualisation scheme, which is suitable both for mission planning and pre-tactical UTM operations. Additionally, the proposed performance-based methodology can accommodate the ersity of infrastructure and vehicle performance requirements currently envisaged in the UTM context. This facilitates the adoption of this methodology for low-level airspace integration of UAS (which may differ significantly in terms of their avionics CNS capabilities) and set foundations for future work on tactical online UTM operations.
Publisher: Unpublished
Date: 2015
Publisher: Elsevier BV
Date: 11-2016
Publisher: MDPI AG
Date: 27-09-2019
DOI: 10.3390/S19194209
Abstract: One of the primary challenges facing Urban Air Mobility (UAM) and the safe integration of Unmanned Aircraft Systems (UAS) in the urban airspace is the availability of robust, reliable navigation and Sense-and-Avoid (SAA) systems. Global Navigation Satellite Systems (GNSS) are typically the primary source of positioning for most air and ground vehicles and for a growing number of UAS applications however, their performance is frequently inadequate in such challenging environments. This paper performs a comprehensive analysis of GNSS performance for UAS operations with a focus on failure modes in urban environments. Based on the analysis, a guidance strategy is developed which accounts for the influence of urban structures on GNSS performance. A simulation case study representative of UAS operations in urban environments is conducted to assess the validity of the proposed approach. Results show improved accuracy (approximately 25%) and availability when compared against a conventional minimum-distance guidance strategy.
Publisher: American Institute of Aeronautics and Astronautics
Date: 07-01-2019
Publisher: IEEE
Date: 09-2016
Publisher: Trans Tech Publications, Ltd.
Date: 10-2014
DOI: 10.4028/WWW.SCIENTIFIC.NET/AMM.629.304
Abstract: The design and trajectory computation algorithms of an innovative Flight Management System (FMS) for Unmanned Reusable Space Vehicle (URSV) are presented. The proposed FMS features a number of common functionalities with modern aircraft FMS that enable flight planning in non-segregated airspace, as well as specific features for optimal trajectory generation and space segment monitoring of the flight mission. The general avionics architecture of URSV is presented and the specific FMS algorithms are developed to cope with the flight vehicle optimal trajectory planning and monitoring. Simulation case studies are performed in a realistic operational scenario resulting in the rapid generation of feasible trajectories, ensuring no violation of the defined mission and vehicle dynamics constraints. Additionally, error budget analysis is performed on longitudinal profile trajectories to evaluate the URSV performance.
Publisher: Unpublished
Date: 2015
Publisher: IEEE
Date: 03-10-2021
Publisher: Elsevier BV
Date: 07-2019
Publisher: MDPI AG
Date: 14-02-2019
Abstract: The airworthiness certification of aerospace cyber-physical systems traditionally relies on the probabilistic safety assessment as a standard engineering methodology to quantify the potential risks associated with faults in system components. This paper presents and discusses the probabilistic safety assessment of detect and avoid (DAA) systems relying on multiple cooperative and non-cooperative tracking technologies to identify the risk of collision of unmanned aircraft systems (UAS) with other flight vehicles. In particular, fault tree analysis (FTA) is utilized to measure the overall system unavailability for each basic component failure. Considering the inter-dependencies of navigation and surveillance systems, the common cause failure (CCF)-beta model is applied to calculate the system risk associated with common failures. Additionally, an importance analysis is conducted to quantify the safety measures and identify the most significant component failures. Results indicate that the failure in traffic detection by cooperative surveillance systems contribute more to the overall DAA system functionality and that the probability of failure for ownship locatability in cooperative surveillance is greater than its traffic detection function. Although all the sensors in idually yield 99.9% operational availability, the implementation of adequate multi-sensor DAA system relying on both cooperative and non-cooperative technologies is shown to be necessary to achieve the desired levels of safety in all possible encounters. These results strongly support the adoption of a unified analytical framework for cooperative/non-cooperative UAS DAA and elicits an evolution of the current certification framework to properly account for artificial intelligence and machine-learning based systems.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2017
Publisher: Unpublished
Date: 2014
Publisher: IEEE
Date: 09-2017
Publisher: Elsevier BV
Date: 04-2003
Publisher: Unpublished
Date: 2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Unpublished
Date: 2014
Publisher: IEEE
Date: 09-2020
Publisher: Marques Aviation Ltd.
Date: 07-2013
Publisher: Marques Aviation Ltd.
Date: 07-2013
Publisher: MDPI AG
Date: 09-10-2019
DOI: 10.3390/S19204361
Abstract: This paper presents a sensor-orientated approach to on-orbit position uncertainty generation and quantification for both ground-based and space-based surveillance applications. A mathematical framework based on the least squares formulation is developed to exploit real-time navigation measurements and tracking observables to provide a sound methodology that supports separation assurance and collision avoidance among Resident Space Objects (RSO). In line with the envisioned Space Situational Awareness (SSA) evolutions, the method aims to represent the navigation and tracking errors in the form of an uncertainty volume that accurately depicts the size, shape, and orientation. Simulation case studies are then conducted to verify under which sensors performance the method meets Gaussian assumptions, with a greater view to the implications that uncertainty has on the cyber-physical architecture evolutions and Cognitive Human-Machine Systems required for Space Situational Awareness and the development of a comprehensive Space Traffic Management framework.
Publisher: FapUNIFESP (SciELO)
Date: 26-02-2018
DOI: 10.5028/JATM.V10.779
Publisher: IEEE
Date: 09-2016
Publisher: American Institute of Aeronautics and Astronautics
Date: 13-08-2012
DOI: 10.2514/6.2012-4829
Publisher: American Institute of Aeronautics and Astronautics
Date: 15-08-2013
DOI: 10.2514/6.2013-4763
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2016
Publisher: MDPI AG
Date: 23-09-2020
DOI: 10.3390/S20195467
Abstract: The continuing development of avionics for Unmanned Aircraft Systems (UASs) is introducing higher levels of intelligence and autonomy both in the flight vehicle and in the ground mission control, allowing new promising operational concepts to emerge. One-to-Many (OTM) UAS operations is one such concept and its implementation will require significant advances in several areas, particularly in the field of Human–Machine Interfaces and Interactions (HMI2). Measuring cognitive load during OTM operations, in particular Mental Workload (MWL), is desirable as it can relieve some of the negative effects of increased automation by providing the ability to dynamically optimize avionics HMI2 to achieve an optimal sharing of tasks between the autonomous flight vehicles and the human operator. The novel Cognitive Human Machine System (CHMS) proposed in this paper is a Cyber-Physical Human (CPH) system that exploits the recent technological developments of affordable physiological sensors. This system focuses on physiological sensing and Artificial Intelligence (AI) techniques that can support a dynamic adaptation of the HMI2 in response to the operators’ cognitive state (including MWL), external/environmental conditions and mission success criteria. However, significant research gaps still exist, one of which relates to a universally valid method for determining MWL that can be applied to UAS operational scenarios. As such, in this paper we present results from a study on measuring MWL on five participants in an OTM UAS wildfire detection scenario, using Electroencephalogram (EEG) and eye tracking measurements. These physiological data are compared with a subjective measure and a task index collected from mission-specific data, which serves as an objective task performance measure. The results show statistically significant differences for all measures including the subjective, performance and physiological measures performed on the various mission phases. Additionally, a good correlation is found between the two physiological measurements and the task index. Fusing the physiological data and correlating with the task index gave the highest correlation coefficient (CC = 0.726 ± 0.14) across all participants. This demonstrates how fusing different physiological measurements can provide a more accurate representation of the operators’ MWL, whilst also allowing for increased integrity and reliability of the system.
Publisher: IEEE
Date: 10-2009
Publisher: Springer Science and Business Media LLC
Date: 11-06-2018
Publisher: IEEE
Date: 06-2019
Publisher: IEEE
Date: 06-2019
Publisher: Unpublished
Date: 2015
Publisher: IEEE
Date: 04-2015
Publisher: Elsevier BV
Date: 05-2012
Publisher: American Institute of Aeronautics and Astronautics
Date: 15-08-2013
DOI: 10.2514/6.2013-4893
Publisher: IEEE
Date: 06-2018
Publisher: Trans Tech Publications, Ltd.
Date: 10-2014
DOI: 10.4028/WWW.SCIENTIFIC.NET/AMM.629.48
Abstract: Experiments at low Reynolds numbers were performed on a pressure tapped NACA 2313 wing in a 3 x 2 x 9 meter wind tunnel under nominally smooth (Ti = 1.2%) and turbulent (Ti = 7.2%) flows at a mean flow velocity of 8ms -1 (Re ≈120,000). The NACA 2313 wing is a replica of Micro Air Vehicle (MAV) wing of the Flash 3D aircraft used at RMIT University for research purposes. Unsteady surface pressures were measured to understand if the information could be adopted for resolving turbulence-induced perturbations and to furthermore use it in a turbulence mitigation system. Two span-wise locations of chord-wise pressure were acquired when tested under the two different flow conditions. It was discovered that at both span-wise locations, a local Coefficient of Pressure (Cp) held high correlation to the chord-wise Cp integration and allowed for a linear relationship to be formed between the two variables. The defined relationship provided a 95% confidence for angles of attack below stall and was used to estimate the integrated chord-wise pressure coefficient at a particular span wise location. The relationship between a single pressure tap and the integrated Cp of that chord-wise section was valid for the two different span-wise locations with similar defining equations. As one pressure tap is sufficient to adequately estimate the integrated Cp on a chord-wise wing section, a limited amount of pressure taps across the wings span approximates the pressure distribution across the span and eventually approximates the flight perturbations. Being a novel method of sensing aircraft disturbance, applications are not restricted to MAV. The methodology presented could very well be applied to larger aircraft to reduce the effects of turbulence within the terminal area and can provide other means of active stabilization.
Publisher: Institution of Engineering and Technology
Date: 20-04-2020
DOI: 10.1049/PBCE120G_CH2
Publisher: MDPI AG
Date: 17-10-2020
DOI: 10.3390/S20205892
Abstract: Automated collection of on-vehicle sensor data allows the development of artificial intelligence (AI) techniques for vehicular systems’ diagnostic and prognostic processes to better assess the state-of-health, predict faults and evaluate residual life of ground vehicle systems. One of the vital subsystems, in terms of safety and mission criticality, is the power train, (comprising the engine, transmission, and final drives), which provides the driving torque required for vehicle acceleration. In this paper, a novel health and usage monitoring system (HUMS) architecture is presented, together with dedicated diagnosis rognosis algorithms that utilize data gathered from a sensor network embedded in an armoured personnel carrier (APC) vehicle. To model the drivetrain, a virtual dynamometer is introduced, which estimates the engine torque output for successive comparison with the measured torque values taken from the engine control unit. This virtual dynamometer is also used in conjunction with other sensed variables to determine the maximum torque output of the engine, which is considered to be the primary indicator of engine health. Regression analysis is performed to capture the effect of certain variables such as engine hours, oil temperature, and coolant temperature on the degradation of maximum engine torque. Degradations in the final drives system were identified using a comparison of the temperature trends between the left-hand and right-hand final drives. This research lays foundations for the development of real-time diagnosis and prognosis functions for an integrated vehicle health management (IVHM) system suitable for safety critical manned and unmanned vehicle applications.
Publisher: IEEE
Date: 09-2016
Publisher: Unpublished
Date: 2014
Publisher: IEEE
Date: 09-2017
Publisher: MDPI AG
Date: 07-02-2018
DOI: 10.3390/S18020499
Publisher: Unpublished
Date: 2015
Publisher: Springer Science and Business Media LLC
Date: 22-09-2017
Publisher: IEEE
Date: 05-2014
Publisher: Elsevier BV
Date: 10-2018
Publisher: Walter de Gruyter GmbH
Date: 29-12-2012
DOI: 10.2478/S13531-012-0033-1
Abstract: Novel techniques for laser beam atmospheric extinction measurements, suitable for several air and space platform applications, are presented in this paper. Extinction measurements are essential to support the engineering development and the operational employment of a variety of aerospace electro-optical sensor systems, allowing calculation of the range performance attainable with such systems in current and likely future applications. Such applications include ranging, weaponry, Earth remote sensing and possible planetary exploration missions performed by satellites and unmanned flight vehicles. Unlike traditional LIDAR methods, the proposed techniques are based on measurements of the laser energy (intensity and spatial distribution) incident on target surfaces of known geometric and reflective characteristics, by means of infrared detectors and/or infrared cameras calibrated for radiance. Various laser sources can be employed with wavelengths from the visible to the far infrared portions of the spectrum, allowing for data correlation and extended sensitivity. Errors affecting measurements performed using the proposed methods are discussed in the paper and algorithms are proposed that allow a direct determination of the atmospheric transmittance and spatial characteristics of the laser spot. These algorithms take into account a variety of linear and non-linear propagation effects. Finally, results are presented relative to some experimental activities performed to validate the proposed techniques. Particularly, data are presented relative to both ground and flight trials performed with laser systems operating in the near infrared (NIR) at λ= 1064 nm and λ= 1550 nm. This includes ground tests performed with 10 Hz and 20 KHz PRF NIR laser systems in a large variety of atmospheric conditions, and flight trials performed with a 10 Hz airborne NIR laser system installed on a TORNADO aircraft, flying up to altitudes of 22,000 ft.
Publisher: MDPI AG
Date: 08-08-2019
DOI: 10.3390/S19163465
Abstract: Intelligent automation and trusted autonomy are being introduced in aerospace cyber-physical systems to support erse tasks including data processing, decision-making, information sharing and mission execution. Due to the increasing level of integration/collaboration between humans and automation in these tasks, the operational performance of closed-loop human-machine systems can be enhanced when the machine monitors the operator’s cognitive states and adapts to them in order to maximise the effectiveness of the Human-Machine Interfaces and Interactions (HMI2). Technological developments have led to neurophysiological observations becoming a reliable methodology to evaluate the human operator’s states using a variety of wearable and remote sensors. The adoption of sensor networks can be seen as an evolution of this approach, as there are notable advantages if these sensors collect and exchange data in real-time, while their operation is controlled remotely and synchronised. This paper discusses recent advances in sensor networks for aerospace cyber-physical systems, focusing on Cognitive HMI2 (CHMI2) implementations. The key neurophysiological measurements used in this context and their relationship with the operator’s cognitive states are discussed. Suitable data analysis techniques based on machine learning and statistical inference are also presented, as these techniques allow processing both neurophysiological and operational data to obtain accurate cognitive state estimations. Lastly, to support the development of sensor networks for CHMI2 applications, the paper addresses the performance characterisation of various state-of-the-art sensors and the propagation of measurement uncertainties through a machine learning-based inference engine. Results show that a proper sensor selection and integration can support the implementation of effective human-machine systems for various challenging aerospace applications, including Air Traffic Management (ATM), commercial airliner Single-Pilot Operations (SIPO), one-to-many Unmanned Aircraft Systems (UAS), and space operations management.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2021
Publisher: Elsevier BV
Date: 12-2019
Publisher: Elsevier BV
Date: 08-2017
Publisher: IEEE
Date: 04-2015
Publisher: MDPI AG
Date: 28-10-2021
Abstract: This paper addresses one of the recognized barriers to the unrestricted adoption of Unmanned Aircraft (UA) in mainstream urban use—noise—and reviews existing approaches for estimating and mitigating this problem. The aircraft noise problem is discussed upfront in general terms by introducing the sound emission, propagation, and psychoacoustic effects. The propagation of sound in the atmosphere, which is the focus of this paper, is then analysed in detail to isolate the environmental and operational factors that predominantly influence the perceived noise on the ground, especially looking at large-scale low-altitude UA operations, such as in the envisioned Urban Air Mobility (UAM) concepts. The physics of sound propagation are presented, considering all attenuation effects and the anomalies due to Doppler and atmospheric effects, such as wind, thermal inversion, and turbulence. The analysis allows to highlight the limitations of current mainstream aircraft noise modelling and certification approaches and, in particular, their inadequacy in addressing the noise of UA and, more generally, UAM vehicles. This finding is important considering that, although reducing noise at the source has remained a priority for manufacturers to enable the scaling up of UAM and drone delivery operations in the near future, the impact of poorly considered propagation and psychoacoustic effects on the actual perceived noise on the ground is equally important for the same objective. For instance, optimizing the flight paths as a function of local weather conditions can significantly contribute to minimizing the impact of noise on communities, thus paving the way for the introduction of full-scale UAM operations. A more reliable and accurate modelling of noise ground signatures for both manned and unmanned low-flying aircraft will aid in identifying the real-time data stream requirements from distributed sensors on the ground. New developments in surrogate sound propagation models, more pervasive real-time sensor data, and suitable computing resources are expected to both yield more reliable and effective estimates of noise reaching the ground listeners and support a dynamic planning of flight paths.
Publisher: SAE International
Date: 15-09-2015
DOI: 10.4271/2015-01-2392
Publisher: MDPI AG
Date: 07-01-2021
Abstract: Advances in unmanned aircraft systems (UAS) have paved the way for progressively higher levels of intelligence and autonomy, supporting new modes of operation, such as the one-to-many (OTM) concept, where a single human operator is responsible for monitoring and coordinating the tasks of multiple unmanned aerial vehicles (UAVs). This paper presents the development and evaluation of cognitive human-machine interfaces and interactions (CHMI2) supporting adaptive automation in OTM applications. A CHMI2 system comprises a network of neurophysiological sensors and machine-learning based models for inferring user cognitive states, as well as the adaptation engine containing a set of transition logics for control/display functions and discrete autonomy levels. Models of the user’s cognitive states are trained on past performance and neurophysiological data during an offline calibration phase, and subsequently used in the online adaptation phase for real-time inference of these cognitive states. To investigate adaptive automation in OTM applications, a scenario involving bushfire detection was developed where a single human operator is responsible for tasking multiple UAV platforms to search for and localize bushfires over a wide area. We present the architecture and design of the UAS simulation environment that was developed, together with various human-machine interface (HMI) formats and functions, to evaluate the CHMI2 system’s feasibility through human-in-the-loop (HITL) experiments. The CHMI2 module was subsequently integrated into the simulation environment, providing the sensing, inference, and adaptation capabilities needed to realise adaptive automation. HITL experiments were performed to verify the CHMI2 module’s functionalities in the offline calibration and online adaptation phases. In particular, results from the online adaptation phase showed that the system was able to support real-time inference and human-machine interface and interaction (HMI2) adaptation. However, the accuracy of the inferred workload was variable across the different participants (with a root mean squared error (RMSE) ranging from 0.2 to 0.6), partly due to the reduced number of neurophysiological features available as real-time inputs and also due to limited training stages in the offline calibration phase. To improve the performance of the system, future work will investigate the use of alternative machine learning techniques, additional neurophysiological input features, and a more extensive training stage.
Publisher: Inderscience Publishers
Date: 2017
Publisher: Walter de Gruyter GmbH
Date: 12-2012
DOI: 10.2478/V10367-012-0019-3
Abstract: In this paper we present a new low-cost navigation system designed for small size Unmanned Aerial Vehicles (UAVs) based on Vision-Based Navigation (VBN) and other avionics sensors. The main objective of our research was to design a compact, light and relatively inexpensive system capable of providing the Required Navigation Performance (RNP) in all phases of flight of a small UAV, with a special focus on precision approach and landing, where Vision Based Navigation (VBN) techniques can be fully exploited in a multisensor integrated architecture. Various existing techniques for VBN were compared and the Appearance-Based Approach (ABA) was selected for implementation. Feature extraction and optical flow techniques were employed to estimate flight parameters such as roll angle, pitch angle, deviation from the runway and body rates. Additionally, we addressed the possible synergies between VBN, Global Navigation Satellite System (GNSS) and MEMS-IMU (Micro-Electromechanical System Inertial Measurement Unit) sensors, as well as the aiding from Aircraft Dynamics Models (ADMs). In particular, by employing these sensors/models, we aimed to compensate for the shortcomings of VBN and MEMS-IMU sensors in high-dynamics attitude determination tasks. An Extended Kalman Filter (EKF) was developed to fuse the information provided by the different sensors and to provide estimates of position, velocity and attitude of the UAV platform in real-time. Two different integrated navigation system architectures were implemented. The first used VBN at 20 Hz and GPS at 1 Hz to augment the MEMS-IMU running at 100 Hz. The second mode also included the ADM (computations performed at 100 Hz) to provide augmentation of the attitude channel. Simulation of these two modes was accomplished in a significant portion of the AEROSONDE UAV operational flight envelope and performing a variety of representative manoeuvres (i.e., straight climb, level turning, turning descent and climb, straight descent, etc.). Simulation of the first integrated navigation system architecture (VBN/IMU/GPS) showed that the integrated system can reach position, velocity and attitude accuracies compatible with CAT-II precision approach requirements. Simulation of the second system architecture (VBN/IMU/GPS/ADM) also showed promising results since the achieved attitude accuracy was higher using the ADM/VBS/IMU than using VBS/IMU only. However, due to rapid ergence of the ADM virtual sensor, there was a need for frequent re-initialisation of the ADM data module, which was strongly dependent on the UAV flight dynamics and the specific manoeuvring transitions performed
Publisher: Inderscience Publishers
Date: 2016
Publisher: Elsevier BV
Date: 05-2015
Publisher: Cambridge University Press (CUP)
Date: 21-03-2013
DOI: 10.1017/S0373463313000027
Abstract: The aviation community has very stringent navigation integrity requirements that apply to a variety of manned and Unmanned Aerial Vehicle (UAV) operational tasks. This paper presents the results of the research activities carried out by the Italian Air Force Flight Test Centre (CSV-RSV) in collaboration with the Nottingham Geospatial Institute (NGI) and Cranfield University (CU) in the area of Avionics-Based Integrity Augmentation (ABIA) for mission- and safety-critical Global Navigation Satellite System (GNSS) applications. Based on these activities, suitable models were developed to describe the main causes of GNSS signal outage and degradation in flight, namely: antenna obscuration, multipath, fading due to adverse geometry and Doppler shift. Adopting these models in association with suitable integrity thresholds and guidance algorithms, the ABIA system delivers integrity caution (predictive) and warning (reactive) flags, as well as steering information to the pilot and electronic commands to the aircraft/UAV flight control system. These features allow real-time avoidance of safety-critical flight conditions and fast recovery of the required navigation performance in case of GNSS data losses. This paper presents the key ABIA concepts, architecture and mathematical models. A successive paper will address the ABIA integrity thresholds criteria and detailed results of a TORNADO simulation case-study.
Publisher: IEEE
Date: 05-2014
Publisher: MDPI AG
Date: 10-2018
Abstract: Resurgent interest in artificial intelligence (AI) techniques focused research attention on their application in aviation systems including air traffic management (ATM), air traffic flow management (ATFM), and unmanned aerial systems traffic management (UTM). By considering a novel cognitive human–machine interface (HMI), configured via machine learning, we examined the requirements for such techniques to be deployed operationally in an ATM system, exploring aspects of vendor verification, regulatory certification, and end-user acceptance. We conclude that research into related fields such as explainable AI (XAI) and computer-aided verification needs to keep pace with applied AI research in order to close the research gaps that could hinder operational deployment. Furthermore, we postulate that the increasing levels of automation and autonomy introduced by AI techniques will eventually subject ATM systems to certification requirements, and we propose a means by which ground-based ATM systems can be accommodated into the existing certification framework for aviation systems.
Publisher: MDPI AG
Date: 07-10-2023
DOI: 10.3390/RS15194855
Publisher: Wiley
Date: 14-06-2015
Publisher: Walter de Gruyter GmbH
Date: 06-2013
Abstract: This paper presents the second part of the research activity performed by Cranfield University to assess the potential of low-cost navigation sensors for Unmanned Aerial Vehicles (UAVs). This part focuses on carrier-phase Global Navigation Satellite Systems (GNSS) for attitude determination and control of small to medium size UAVs. Recursive optimal estimation algorithms were developed for combining multiple attitude measurements obtained from different observation points (i.e., antenna locations), and their efficiencies were tested in various dynamic conditions. The proposed algorithms converged rapidly and produced the required output even during high dynamics manoeuvres. Results of theoretical performance analysis and simulation activities are presented in this paper, with emphasis on the advantages of the GNSS interferometric approach in UAV applications (i.e., low cost, high data-rate, low volume/weight, low signal processing requirements, etc.). The simulation activities focussed on the AEROSONDE UAV platform and considered the possible augmentation provided by interferometric GNSS techniques to a low-cost and low-weight/volume integrated navigation system (presented in the first part of this series) which employed a Vision-Based Navigation (VBN) system, a Micro-Electro-Mechanical Sensor (MEMS) based Inertial Measurement Unit (IMU) and code-range GNSS (i.e., GPS and GALILEO) for position and velocity computations. The integrated VBN-IMU-GNSS (VIG) system was augmented using the inteferometric GNSS Attitude Determination (GAD) sensor data and a comparison of the performance achieved with the VIG and VIG/GAD integrated Navigation and Guidance Systems (NGS) is presented in this paper. Finally, the data provided by these NGS are used to optimise the design of a hybrid controller employing Fuzzy Logic and Proportional-Integral-Derivative (PID) techniques for the AEROSONDE UAV.
Publisher: IEEE
Date: 06-2015
Publisher: Trans Tech Publications, Ltd.
Date: 10-2014
DOI: 10.4028/WWW.SCIENTIFIC.NET/AMM.629.164
Abstract: A method for deriving the parameters of a six-degree-of-freedom (6-DoF) aircraft dynamics model by adopting reverse engineering techniques is presented. The novelty of the paper is the adaption of the 6-DoF Aircraft Dynamics Model (ADM) as a virtual sensor integrated in a low-cost navigation and guidance system designed for small Unmanned Aircraft (UA). The mass and aerodynamic properties of the JAVELIN UA are determined with the aid of an accurate 3D scanning and CAD processing. For qualitatively assessing the calculated ADM, a trajectory with high dynamics is simulated for the JAVELIN UA and compared with that of a published 6-DoF model of the AEROSONDE UA. Additionally, to confirm the validity of the approach, reverse engineering procedures are applied to a published CAD model of the AEROSONDE UA aiding to the calculation of the associated 6-DoF model parameters. A spiral descent trajectory is generated using both the published and calculated parameters of the AEROSONDE UA and a comparative analysis is performed that validates the methodology. The accurate knowledge of the ADM is then utilized in the development of a virtual sensor to augment the UA navigation and guidance system in case of primary navigation sensor outages.
Publisher: IEEE
Date: 09-2016
Publisher: Springer Science and Business Media LLC
Date: 13-10-2018
Publisher: MDPI AG
Date: 16-06-2021
DOI: 10.3390/COMPUTERS10060081
Abstract: With increasingly higher levels of automation in aerospace decision support systems, it is imperative that the human operator maintains the required level of situational awareness in different operational conditions and a central role in the decision-making process. While current aerospace systems and interfaces are limited in their adaptability, a Cognitive Human Machine System (CHMS) aims to perform dynamic, real-time system adaptation by estimating the cognitive states of the human operator. Nevertheless, to reliably drive system adaptation of current and emerging aerospace systems, there is a need to accurately and repeatably estimate cognitive states, particularly for Mental Workload (MWL), in real-time. As part of this study, two sessions were performed during a Multi-Attribute Task Battery (MATB) scenario, including a session for offline calibration and validation and a session for online validation of eleven multimodal inference models of MWL. The multimodal inference model implemented included an Adaptive Neuro Fuzzy Inference System (ANFIS), which was used in different configurations to fuse data from an Electroencephalogram (EEG) model’s output, four eye activity features and a control input feature. The online validation of the ANFIS models produced good results, while the best performing model (containing all four eye activity features and the control input feature) showed an average Mean Absolute Error (MAE) = 0.67 ± 0.18 and Correlation Coefficient (CC) = 0.71 ± 0.15. The remaining six ANFIS models included data from the EEG model’s output, which had an offset discrepancy. This resulted in an equivalent offset for the online multimodal fusion. Nonetheless, the efficacy of these ANFIS models could be confirmed by the pairwise correlation with the task level, where one model demonstrated a CC = 0.77 ± 0.06, which was the highest among all of the ANFIS models tested. Hence, this study demonstrates the suitability for online multimodal fusion of features extracted from EEG signals, eye activity and control inputs to produce an accurate and repeatable inference of MWL.
Publisher: Elsevier BV
Date: 12-2017
Publisher: IEEE
Date: 04-2015
Publisher: Cambridge University Press (CUP)
Date: 03-05-2013
DOI: 10.1017/S0373463313000143
Abstract: This paper presents the second part of the research activities carried out to develop a novel Global Navigation Satellite System (GNSS) Avionics-Based Integrity Augmentation (ABIA) system for manned and Unmanned Aerial Vehicle (UAV) applications. The ABIA system's architecture was developed to allow real-time avoidance of safety-critical flight conditions and fast recovery of the required navigation performance in case of GNSS data losses. In more detail, our novel ABIA system addresses all four cornerstones of GNSS integrity augmentation in mission- and safety-critical avionics applications: prediction (caution flags), avoidance (optimal flight path guidance), reaction (warning flags) and correction (recovery flight path guidance). Part 1 (Sabatini et al., 2012) presented the ABIA concept, architecture and key mathematical models used to describe GNSS integrity issues in aircraft applications. This second part addresses the ABIA caution and warning integrity flags criteria and presents the results of a simulation case study performed on the TORNADO Interdiction and Strike (IDS) aircraft.
Publisher: Elsevier BV
Date: 11-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: American Institute of Aeronautics and Astronautics
Date: 15-08-2013
DOI: 10.2514/6.2013-4970
Publisher: Elsevier BV
Date: 05-2022
Publisher: American Institute of Aeronautics and Astronautics
Date: 04-01-2021
DOI: 10.2514/6.2021-0658
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2020
Publisher: Unpublished
Date: 2014
Publisher: SAE International
Date: 15-09-2015
DOI: 10.4271/2015-01-2459
Publisher: IEEE
Date: 05-2014
Publisher: SAE International
Date: 15-09-2015
DOI: 10.4271/2015-01-2456
Publisher: Institution of Engineering and Technology
Date: 20-04-2020
DOI: 10.1049/PBCE120F_CH4
Publisher: Institution of Engineering and Technology
Date: 20-04-2020
DOI: 10.1049/PBCE120F_CH1
Publisher: Elsevier BV
Date: 05-2021
Publisher: Springer International Publishing
Date: 2016
Publisher: MDPI AG
Date: 15-02-2023
Abstract: The accurate modelling and simulation of vehicle dynamics is a fundamental prerequisite for the design and experimental flight testing of aerospace vehicles. In the case of high-altitude supersonic sounding rockets, it is critically important to produce realistic trajectory predictions in a representative range of operational and environmental conditions as well as to produce reliable probability distributions of terminal locations. This article proposes a methodology to develop high-fidelity flight dynamics models that accurately capture aeroelastic, turbulence, atmospheric and other effects relevant to sounding rockets. The significance of establishing a high-fidelity model and of addressing such a problem in the context of developing a digital twin are discussed upfront, together with the key tools utilised in the analysis. In addition to state-of-the-art computational methods to determine the aerodynamic forces, moments and mass changes in various flight regimes (including parachute release), a detailed methodology for incorporating the dynamic aeroelastic response of the rocket is presented. The validity of the proposed method is demonstrated through a simulation case study, which utilises data from an existing rocket prototype. Results corroborate the correct implementation of the proposed algorithms and provide foundations for future research on virtual sensing and digital twin for autonomous navigation and guidance.
Publisher: IEEE
Date: 11-10-2020
Publisher: IEEE
Date: 03-10-2021
Publisher: SAE International
Date: 17-09-2013
DOI: 10.4271/2013-01-2086
Publisher: Institution of Engineering and Technology
Date: 20-04-2020
DOI: 10.1049/PBCE120F_CH8
Publisher: Institution of Engineering and Technology
Date: 20-04-2020
DOI: 10.1049/PBCE120F_CH6
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2018
Publisher: IEEE
Date: 06-2015
Publisher: Elsevier BV
Date: 11-2014
Publisher: Elsevier BV
Date: 10-2014
Publisher: Inderscience Publishers
Date: 2016
Publisher: Unpublished
Date: 2015
Publisher: Unpublished
Date: 2012
Publisher: Springer Science and Business Media LLC
Date: 13-04-2017
Publisher: MDPI AG
Date: 22-03-2023
DOI: 10.3390/RS15061714
Abstract: Autonomous navigation (AN) and manoeuvring are increasingly important in distributed satellite systems (DSS) in order to avoid potential collisions with space debris and other resident space objects (RSO). In order to accomplish collision avoidance manoeuvres, tracking and characterization of RSO is crucial. At present, RSO are tracked and catalogued using ground-based observations, but space-based space surveillance (SBSS) represents a valid alternative (or complementary asset) due to its ability to offer enhanced performances in terms of sensor resolution, tracking accuracy, and weather independence. This paper proposes a particle swarm optimization (PSO) algorithm for DSS AN and manoeuvring, specifically addressing RSO tracking and collision avoidance requirements as an integral part of the overall system design. More specifically, a DSS architecture employing hyperspectral sensors for Earth observation is considered, and passive electro-optical sensors are used, in conjunction with suitable mathematical algorithms, to accomplish autonomous RSO tracking and classification. Simulation case studies are performed to investigate the tracking and system collision avoidance capabilities in both space-based and ground-based tracking scenarios. Results corroborate the effectiveness of the proposed AN technique and highlight its potential to supplement either conventional (ground-based) or SBSS tracking methods.
Publisher: Elsevier BV
Date: 12-2019
Publisher: MDPI AG
Date: 08-10-2020
DOI: 10.3390/S20195718
Abstract: This paper addresses some of the existing research gaps in the practical use of acoustic waves for navigation of autonomous air and surface vehicles. After providing a characterisation of ultrasonic transducers, a multistatic sensor arrangement is discussed, with multiple transmitters broadcasting their respective signals in a round-robin fashion, following a time ision multiple access (TDMA) scheme. In particular, an optimisation methodology for the placement of transmitters in a given test volume is presented with the objective of minimizing the position dilution of precision (PDOP) and maximizing the sensor availability. Additionally, the contribution of platform dynamics to positioning error is also analysed in order to support future ground and flight vehicle test activities. Results are presented of both theoretical and experimental data analysis performed to determine the positioning accuracy attainable from the proposed multistatic acoustic navigation sensor. In particular, the ranging errors due to signal delays and attenuation of sound waves in air are analytically derived, and static indoor positioning tests are performed to determine the positioning accuracy attainable with different transmitter–receiver-relative geometries. Additionally, it is shown that the proposed transmitter placement optimisation methodology leads to increased accuracy and better coverage in an indoor environment, where the required position, velocity, and time (PVT) data cannot be delivered by satellite-based navigation systems.
Publisher: Unpublished
Date: 2016
Publisher: Elsevier BV
Date: 03-2017
Publisher: Elsevier BV
Date: 2022
Publisher: Unpublished
Date: 2014
Publisher: SAE International
Date: 15-09-2015
DOI: 10.4271/2015-01-2470
Publisher: IEEE
Date: 09-2019
Publisher: SAE International
Date: 15-09-2015
DOI: 10.4271/2015-01-2477
Publisher: SAE International
Date: 15-09-2015
DOI: 10.4271/2015-01-2475
Publisher: Institute of Navigation
Date: 08-11-2016
DOI: 10.33012/2016.14624
Publisher: Elsevier BV
Date: 02-2022
Publisher: Trans Tech Publications, Ltd.
Date: 10-2014
DOI: 10.4028/WWW.SCIENTIFIC.NET/AMM.629.257
Abstract: In this paper, we investigate an innovative application of Light Detection and Ranging (LIDAR) technology for the aviation-related pollutant measurements. The proposed measurement technique is conceived for high-resolution characterisation in space and time domains of aviation-related pollutant gases. The system performs Integral Path Differential Absorption (IPDA) measurement in a bistatic LIDAR measurement setup. The airborne component consists of a tuneable Near Infrared (NIR) laser emitter installed on an Unmanned Aircraft (UA) and the ground subsystem is composed by a target reference surface (calibrated for reflectance) and a differential transmittance measuring device based on a NIR Camera calibrated for radiance. The specific system implementation for Carbon Dioxide (CO 2 ) measurement is discussed. A preliminary assessment of the error figures associated with the proposed system layout is performed.
Publisher: Elsevier BV
Date: 2020
DOI: 10.1016/J.NEULET.2019.134575
Abstract: P2X
Publisher: MDPI AG
Date: 22-03-2023
DOI: 10.3390/S23063344
Abstract: Recent developments in Distributed Satellite Systems (DSS) have undoubtedly increased mission value due to the ability to reconfigure the spacecraft cluster/formation and incrementally add new or update older satellites in the formation. These features provide inherent benefits, such as increased mission effectiveness, multi-mission capabilities, design flexibility, and so on. Trusted Autonomous Satellite Operation (TASO) are possible owing to the predictive and reactive integrity features offered by Artificial Intelligence (AI), including both on-board satellites and in the ground control segments. To effectively monitor and manage time-critical events such as disaster relief missions, the DSS must be able to reconfigure autonomously. To achieve TASO, the DSS should have reconfiguration capability within the architecture and spacecraft should communicate with each other through an Inter-Satellite Link (ISL). Recent advances in AI, sensing, and computing technologies have resulted in the development of new promising concepts for the safe and efficient operation of the DSS. The combination of these technologies enables trusted autonomy in intelligent DSS (iDSS) operations, allowing for a more responsive and resilient approach to Space Mission Management (SMM) in terms of data collection and processing, especially when using state-of-the-art optical sensors. This research looks into the potential applications of iDSS by proposing a constellation of satellites in Low Earth Orbit (LEO) for near-real-time wildfire management. For spacecraft to continuously monitor Areas of Interest (AOI) in a dynamically changing environment, satellite missions must have extensive coverage, revisit intervals, and reconfiguration capability that iDSS can offer. Our recent work demonstrated the feasibility of AI-based data processing using state-of-the-art on-board astrionics hardware accelerators. Based on these initial results, AI-based software has been successively developed for wildfire detection on-board iDSS satellites. To demonstrate the applicability of the proposed iDSS architecture, simulation case studies are performed considering different geographic locations.
Publisher: Elsevier BV
Date: 03-2017
Publisher: IEEE
Date: 09-2017
Publisher: IEEE
Date: 11-10-2020
Publisher: Wiley
Date: 18-07-2016
Publisher: Elsevier BV
Date: 03-2017
Publisher: American Institute of Aeronautics and Astronautics
Date: 06-01-2019
DOI: 10.2514/6.2019-1930
Publisher: Unpublished
Date: 2012
Publisher: Unpublished
Date: 2014
Publisher: IEEE
Date: 11-01-2006
Publisher: Institute of Navigation
Date: 08-11-2016
DOI: 10.33012/2016.14615
Publisher: Institute of Navigation
Date: 08-11-2016
DOI: 10.33012/2016.14616
Publisher: Unpublished
Date: 2014
Publisher: IEEE
Date: 06-2015
Publisher: Springer International Publishing
Date: 2016
Publisher: MDPI AG
Date: 12-08-2021
Abstract: Advances in the trusted autonomy of air-traffic management (ATM) systems are currently being pursued to cope with the predicted growth in air-traffic densities in all classes of airspace. Highly automated ATM systems relying on artificial intelligence (AI) algorithms for anomaly detection, pattern identification, accurate inference, and optimal conflict resolution are technically feasible and demonstrably able to take on a wide variety of tasks currently accomplished by humans. However, the opaqueness and inexplicability of most intelligent algorithms restrict the usability of such technology. Consequently, AI-based ATM decision-support systems (DSS) are foreseen to integrate eXplainable AI (XAI) in order to increase interpretability and transparency of the system reasoning and, consequently, build the human operators’ trust in these systems. This research presents a viable solution to implement XAI in ATM DSS, providing explanations that can be appraised and analysed by the human air-traffic control operator (ATCO). The maturity of XAI approaches and their application in ATM operational risk prediction is investigated in this paper, which can support both existing ATM advisory services in uncontrolled airspace (Classes E and F) and also drive the inflation of avoidance volumes in emerging performance-driven autonomy concepts. In particular, aviation occurrences and meteorological databases are exploited to train a machine learning (ML)-based risk-prediction tool capable of real-time situation analysis and operational risk monitoring. The proposed approach is based on the XGBoost library, which is a gradient-boost decision tree algorithm for which post-hoc explanations are produced by SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). Results are presented and discussed, and considerations are made on the most promising strategies for evolving the human–machine interactions (HMI) to strengthen the mutual trust between ATCO and systems. The presented approach is not limited only to conventional applications but also suitable for UAS-traffic management (UTM) and other emerging applications.
Publisher: Inderscience Publishers
Date: 2017
Publisher: IEEE
Date: 03-10-2021
Publisher: IEEE
Date: 03-10-2021
Publisher: Unpublished
Date: 2015
Publisher: Elsevier BV
Date: 05-2020
Publisher: IEEE
Date: 06-2015
Publisher: MDPI AG
Date: 14-02-2023
DOI: 10.3390/AEROSPACE10020176
Abstract: Space-based Earth Observation (EO) systems have undergone a continuous evolution in the twenty-first century. With the help of space-based Maritime Domain Awareness (MDA), specially Automatic Identification Systems (AIS), their applicability across the world’s waterways, among others, has grown substantially. This research work explores the potential applicability of Synthetic Aperture Radar (SAR) and Distributed Satellite Systems (DSS) for the MDA operation. A robust multi-baseline Along-Track Interferometric Synthetic Aperture Radar (AT-InSAR) Formation Flying concept is proposed to combine several along-track baseline observations effectively for single-pass interferometry. Simulation results are presented to support the feasibility of implementing this acquisition mode with autonomous orbit control, using low-thrust actuation suitable for electric propulsion. To improve repeatability, a constellation of this formation concept is also proposed to combine the benefits of the DSS. An MDA application is considered as a hypothetical mission to be solved by this combined approach.
Publisher: Unpublished
Date: 2014
Publisher: Emerald
Date: 05-10-2015
Publisher: American Institute of Aeronautics and Astronautics
Date: 15-06-2008
DOI: 10.2514/6.2008-7670
Publisher: Springer Science and Business Media LLC
Date: 15-11-2017
Publisher: MDPI AG
Date: 17-07-2021
Abstract: Atmospheric effects have a significant impact on the performance of airborne and space laser systems. Traditional models used to predict propagation effects rely heavily on simplified assumptions of the atmospheric properties and their interactions with laser systems. In the engineering domain, these models need to be continually improved in order to develop tools that can predict laser beam propagation with high accuracy and for a wide range of practical applications such as LIDAR (light detection and ranging), free-space optical communications, remote sensing, etc. The underlying causes of laser beam attenuation in the atmosphere are examined in this paper, with a focus on the dominant linear effects: absorption, scattering, turbulence, and non-linear thermal effects such as blooming, kinetic cooling, and bleaching. These phenomena are quantitatively analyzed, highlighting the implications of the various assumptions made in current modeling approaches. Absorption and scattering, as the dominant causes of attenuation, are generally well captured in existing models and tools, but the impacts of non-linear phenomena are typically not well described as they tend to be application specific. Atmospheric radiative transfer codes, such as MODTRAN, ARTS, etc., and the associated spectral databases, such as HITRAN, are the existing tools that implement state-of-the-art models to quantify the total propagative effects on laser systems. These tools are widely used to analyze system performance, both for design and test/evaluation purposes. However, present day atmospheric radiative transfer codes make several assumptions that reduce accuracy in favor of faster processing. In this paper, the atmospheric radiative transfer models are reviewed highlighting the associated methodologies, assumptions, and limitations. Empirical models are found to offer a robust analysis of atmospheric propagation, which is particularly well-suited for design, development, test and evaluation (DDT& E) purposes. As such, empirical, semi-empirical, and ensemble methodologies are recommended to complement and augment the existing atmospheric radiative transfer codes. There is scope to evolve the numerical codes and empirical approaches to better suit aerospace applications, where fast analysis is required over a range of slant paths, incidence angles, altitudes, and atmospheric conditions, which are not exhaustively captured in current performance assessment methods.
Publisher: IEEE
Date: 06-2016
Publisher: MDPI AG
Date: 21-06-2022
DOI: 10.3390/S22134673
Abstract: Emerging Air Traffic Management (ATM) and avionics human–machine system concepts require the real-time monitoring of the human operator to support novel task assessment and system adaptation features. To realise these advanced concepts, it is essential to resort to a suite of sensors recording neurophysiological data reliably and accurately. This article presents the experimental verification and performance characterisation of a cardiorespiratory sensor for ATM and avionics applications. In particular, the processed physiological measurements from the designated commercial device are verified against clinical-grade equipment. Compared to other studies which only addressed physical workload, this characterisation was performed also looking at cognitive workload, which poses certain additional challenges to cardiorespiratory monitors. The article also addresses the quantification of uncertainty in the cognitive state estimation process as a function of the uncertainty in the input cardiorespiratory measurements. The results of the sensor verification and of the uncertainty propagation corroborate the basic suitability of the commercial cardiorespiratory sensor for the intended aerospace application but highlight the relatively poor performance in respiratory measurements during a purely mental activity.
Publisher: IEEE
Date: 06-2018
Publisher: SAE International
Date: 15-09-2015
DOI: 10.4271/2015-01-2539
Publisher: Elsevier BV
Date: 08-2019
DOI: 10.1016/J.BBI.2019.05.042
Abstract: Few animal models exist that successfully reproduce several core associative and non-associative behaviours relevant to post-traumatic stress disorder (PTSD), such as long-lasting fear reactions, hyperarousal, and subtle attentional and cognitive dysfunction. As such, these models may lack the face validity required to adequately model pathophysiological features of PTSD such as CNS grey matter loss and neuroinflammation. Here we aimed to investigate in a mouse model of PTSD whether contextual fear conditioning associated with a relatively high intensity footshock exposure induces loss of neuronal dendritic spines in various corticolimbic brain regions, as their regression may help explain grey matter reductions in PTSD patients. Further, we aimed to observe whether these changes were accompanied by alterations in microglial cell number and morphology, and increased expression of complement factors implicated in the mediation of microglial cell-mediated engulfment of dendritic spines. Adult male C57Bl6J mice were exposed to a single electric footshock and subsequently underwent phenotyping of various PTSD-relevant behaviours in the short (day 2-4) and longer-term (day 29-31). 32 days post-exposure the brains of these animals were subjected to Golgi staining of dendritic spines, microglial cell Iba-1 immunohistochemistry and immunofluorescent staining of the complement factors C1q and C4. Shock exposure promoted a lasting contextual fear response, decreased locomotor activity, exaggerated acoustic startle responses indicative of hyperarousal, and a short-term facilitation of sensorimotor gating function. The shock triggered loss of dendritic spines on pyramidal neurons was accompanied by increased microglial cell number and complexity in the medial prefrontal cortex and dorsal hippoc us, but not in the amygdala. Shock also increased expression of C1q in the pyramidal layer of the CA1 region of the hippoc us but not in other brain regions. The present study further elaborates on the face and construct validity of a mouse model of PTSD and provides a good foundation to explore potential molecular interactions between microglia and dendritic spines.
Publisher: SAE International
Date: 15-09-2015
DOI: 10.4271/2015-01-2538
Publisher: IEEE
Date: 03-10-2021
Publisher: Elsevier BV
Date: 11-2015
Publisher: Trans Tech Publications, Ltd.
Date: 10-2014
DOI: 10.4028/WWW.SCIENTIFIC.NET/AMM.629.355
Abstract: class="Abstract" This paper presents an overview of the research activities performed to develop a new scaled variant of the Laser Obstacle Avoidance and Monitoring (LOAM) system for small-to-medium size Unmanned Aircraft (UA) platforms. This LOAM variant (LOAM + ) is proposed as one of the non-cooperative sensors employed in the UA Sense-and-Avoid (SAA) system. After a brief description of the LOAM system architecture, the mathematical models developed for obstacle avoidance and calculation of alternative flight path are presented. Additionally, a new formulation is adopted for defining the uncertainty volumes associated with the detected obstacles. Simulation case studies are carried out to evaluate the performances of the avoidance trajectory generation and optimisation algorithms, which demonstrate the ability of LOAM + to effectively detect and avoid fixed low-level obstacles in the intended path.
Publisher: MDPI AG
Date: 26-01-2023
DOI: 10.3390/RS15030720
Abstract: One of the United Nations (UN) Sustainable Development Goals is climate action (SDG-13), and wildfire is among the catastrophic events that both impact climate change and are aggravated by it. In Australia and other countries, large-scale wildfires have dramatically grown in frequency and size in recent years. These fires threaten the world’s forests and urban woods, cause enormous environmental and property damage, and quite often result in fatalities. As a result of their increasing frequency, there is an ongoing debate over how to handle catastrophic wildfires and mitigate their social, economic, and environmental repercussions. Effective prevention, early warning, and response strategies must be well-planned and carefully coordinated to minimise harmful consequences to people and the environment. Rapid advancements in remote sensing technologies such as ground-based, aerial surveillance vehicle-based, and satellite-based systems have been used for efficient wildfire surveillance. This study focuses on the application of space-borne technology for very accurate fire detection under challenging conditions. Due to the significant advances in artificial intelligence (AI) techniques in recent years, numerous studies have previously been conducted to examine how AI might be applied in various situations. As a result of its special physical and operational requirements, spaceflight has emerged as one of the most challenging application fields. This work contains a feasibility study as well as a model and scenario prototype for a satellite AI system. With the intention of swiftly generating alerts and enabling immediate actions, the detection of wildfires has been studied with reference to the Australian events that occurred in December 2019. Convolutional neural networks (CNNs) were developed, trained, and used from the ground up to detect wildfires while also adjusting their complexity to meet onboard implementation requirements for trusted autonomous satellite operations (TASO). The capability of a 1-dimensional convolution neural network (1-DCNN) to classify wildfires is demonstrated in this research and the results are assessed against those reported in the literature. In order to enable autonomous onboard data processing, various hardware accelerators were considered and evaluated for onboard implementation. The trained model was then implemented in the following: Intel Movidius NCS-2 and Nvidia Jetson Nano and Nvidia Jetson TX2. Using the selected onboard hardware, the developed model was then put into practice and analysis was carried out. The results were positive and in favour of using the technology that has been proposed for onboard data processing to enable TASO on future missions. The findings indicate that data processing onboard can be very beneficial in disaster management and climate change mitigation by facilitating the generation of timely alerts for users and by enabling rapid and appropriate responses.
Publisher: OMICS Publishing Group
Date: 2013
Publisher: OMICS Publishing Group
Date: 2013
Publisher: IEEE
Date: 04-2020
Publisher: Unpublished
Date: 2014
Publisher: IEEE
Date: 06-2015
Publisher: Unpublished
Date: 2015
Publisher: OMICS Publishing Group
Date: 2013
Publisher: SAE International
Date: 15-09-2015
DOI: 10.4271/2015-01-2544
Publisher: Elsevier BV
Date: 2017
Publisher: Elsevier BV
Date: 02-2019
Publisher: Elsevier BV
Date: 08-2016
Publisher: Elsevier BV
Date: 03-2017
Publisher: IEEE
Date: 06-2016
Publisher: IEEE
Date: 06-2016
Publisher: IEEE
Date: 09-2016
Publisher: Trans Tech Publications, Ltd.
Date: 10-2014
DOI: 10.4028/WWW.SCIENTIFIC.NET/AMM.629.344
Abstract: This paper presents the concept of operations, architecture and trajectory optimisation algorithms of a Next Generation Flight Management System (NG-FMS). The NG-FMS is developed for Four Dimensional (4D) Intent Based Operations (IBO) in the next generation Communications, Navigation, Surveillance and Air Traffic Management system (CNS+A) context. The NG-FMS, primarily responsible for the aircraft navigation and guidance task, acts as a key enabler for achieving higher level of operational efficiency and mitigating environmental impacts both in manned and unmanned aircraft applications. The NG-FMS is interoperable with the future ground based 4DT Planning, Negotiation and Validation (4-PNV) systems, enabling automated Trajectory/Intent Based Operations (TBO/IBO). After the NG-FMS architecture is presented, the key mathematical models describing the trajectory generation and optimisation modes are introduced. A detailed error analysis is performed and the uncertainties affecting the nominal trajectories are studied to obtain the total NG-FMS error budgets. These are compared with the Required Navigation Performance (RNP) values for the various operational flight tasks considered.
Publisher: OMICS Publishing Group
Date: 2012
Publisher: Hindawi Limited
Date: 2016
DOI: 10.1155/2016/8985017
Publisher: Unpublished
Date: 2016
Publisher: IEEE
Date: 06-0005
Publisher: IEEE
Date: 05-2013
Publisher: IEEE
Date: 06-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2021
Publisher: IEEE
Date: 06-2015
Publisher: IEEE
Date: 05-2014
Publisher: Unpublished
Date: 2014
Publisher: Unpublished
Date: 2014
Publisher: IEEE
Date: 06-2016
Publisher: Elsevier BV
Date: 11-2016
Publisher: IEEE
Date: 06-2015
Publisher: Inderscience Publishers
Date: 2017
Publisher: IEEE
Date: 05-2014
Publisher: Inderscience Publishers
Date: 2017
Publisher: MDPI AG
Date: 29-12-2020
DOI: 10.3390/S21010171
Abstract: In agriculture, early detection of plant stresses is advantageous in preventing crop yield losses. Remote sensors are increasingly being utilized for crop health monitoring, offering non-destructive, spatialized detection and the quantification of plant diseases at various levels of measurement. Advances in sensor technologies have promoted the development of novel techniques for precision agriculture. As in situ techniques are surpassed by multispectral imaging, refinement of hyperspectral imaging and the promising emergence of light detection and ranging (LIDAR), remote sensing will define the future of biotic and abiotic plant stress detection, crop yield estimation and product quality. The added value of LIDAR-based systems stems from their greater flexibility in capturing data, high rate of data delivery and suitability for a high level of automation while overcoming the shortcomings of passive systems limited by atmospheric conditions, changes in light, viewing angle and canopy structure. In particular, a multi-sensor systems approach and associated data fusion techniques (i.e., blending LIDAR with existing electro-optical sensors) offer increased accuracy in plant disease detection by focusing on traditional optimal estimation and the adoption of artificial intelligence techniques for spatially and temporally distributed big data. When applied across different platforms (handheld, ground-based, airborne, ground/aerial robotic vehicles or satellites), these electro-optical sensors offer new avenues to predict and react to plant stress and disease. This review examines the key sensor characteristics, platform integration options and data analysis techniques recently proposed in the field of precision agriculture and highlights the key challenges and benefits of each concept towards informing future research in this very important and rapidly growing field.
Publisher: IEEE
Date: 06-2018
Publisher: Trans Tech Publications, Ltd.
Date: 10-2014
DOI: 10.4028/WWW.SCIENTIFIC.NET/AMM.629.202
Abstract: One of the key challenges of designing low-cost Unmanned Aircraft Systems (UAS) is to ensure acceptable and certifiable reliability factors for the adopted Commercial-off-the-Shelf (COTS) components since their reliability is often not quantified. In this paper, the experimental results obtained for quantifying the reliability of mini Unmanned Aircraft (UA) servomotors (by recording their time-to-failure on a defined set of test runs) are presented. The Weibull prediction model is adopted for quantitative analysis and the associated key mathematical models. The methodology adopted for performing the reliability analysis including the test bench setup used for the experiments is described. The results indicate a level of reliability expected for low-cost servos. Such servos could be used for low-risk UAS operations (e.g. small UA operating over sparsely populated regions) and where the economics of the business case permitted higher loss rates.
Publisher: MDPI AG
Date: 17-07-2023
DOI: 10.3390/RS15143579
Abstract: There was an error in the original publication [...]
Publisher: Elsevier BV
Date: 2013
Publisher: MDPI AG
Date: 21-07-2022
Abstract: Collision risk modelling has a long history in the aviation industry, with mature models currently utilised for the strategic planning of airspace sectors and air routes. However, the progressive introduction of Unmanned Aircraft Systems (UAS) and other forms of air mobility poses new challenges, compounded by a growing need to address both offline and online operational requirements. To address the associated gaps in the existing airspace risk assessment models, this article proposes a comprehensive risk management framework, which relies on a novel methodology to model UAS collision risk in all classes of airspace. This methodology inherently accounts for the performance of Communication, Navigation and Surveillance (CNS) systems, and, as such, it can be applied to both strategic and tactical operational timeframes. Additionally, the proposed approach can be applied inversely to determine CNS performance requirements given a target value of collision probability. This new risk assessment methodology is based on a rigorous analysis of the CNS error characteristics and transformation of the associated models into the spatial domain to generate a protection volume around each predicted air traffic conflict. Additionally, a methodology to quickly and conservatively evaluate the multi-integral formulation of collision probability is introduced. The validity of the proposed framework is tested using representative CNS performance parameters in two simulation case studies targeting, respectively, a terminal manoeuvring area and an enroute scenario.
Publisher: Unpublished
Date: 2014
Publisher: IEEE
Date: 11-10-2020
Publisher: Trans Tech Publications, Ltd.
Date: 10-2014
DOI: 10.4028/WWW.SCIENTIFIC.NET/AMM.629.327
Abstract: This paper presents models and algorithms for real-time 4-Dimensional Flight Trajectory (4DT) operations in the next generation Communications, Navigation, Surveillance/Air Traffic Management (CNS/ATM) systems. In particular, the models are employed for multi-objective optimisation of 4DT intents in ground-based 4DT Planning, Negotiation and Validation (4-PNV) systems and in airborne Next Generation Flight Management Systems (NG-FMS). The assumed timeframe convention for offline and online air traffic operations is introduced and discussed. The adopted formulation of the multi-objective 4DT optimisation problem includes a number of environmental objectives and operational constraints. In particular, the paper describes a real-time multi-objective optimisation algorithm and the generalised expression of cost function adopted for penalties associated with specific airspace volumes, accounting for weather models, condensation trails models and noise models.
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Arab Emirates
Location: United Kingdom of Great Britain and Northern Ireland
Start Date: 2016
End Date: 2017
Funder: Defence Science and Technology Group
View Funded ActivityStart Date: 2018
End Date: 2019
Funder: Department of Defence, Australian Government
View Funded ActivityStart Date: 2018
End Date: 2021
Funder: Northrop Grumman
View Funded ActivityStart Date: 2019
End Date: 2023
Funder: Thales Group
View Funded ActivityStart Date: 2020
End Date: 2021
Funder: Lockheed Martin STELaRLab
View Funded ActivityStart Date: 2021
End Date: 2022
Funder: Defence Science and Technology Group
View Funded Activity