ORCID Profile
0000-0002-6776-3411
Current Organisation
RMIT University
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Publisher: IEEE
Date: 11-2012
Publisher: Institute of Navigation
Date: 09-1999
Publisher: Walter de Gruyter GmbH
Date: 2013
Publisher: Walter de Gruyter GmbH
Date: 2013
Publisher: Springer Science and Business Media LLC
Date: 23-05-2019
Publisher: IEEE
Date: 11-2012
Publisher: MDPI AG
Date: 22-02-2021
DOI: 10.3390/S21041507
Abstract: This paper considers the two-dimensional (2D) anchorless localization problem for sensor networks in global positioning system (GPS)-denied environments. We present an efficient method, based on the multidimensional scaling (MDS) algorithm, in order to estimate the positions of the nodes in the network using measurements of the inter-node distances. The proposed method takes advantage of the mobility of the nodes to address the location ambiguity problem, i.e., rotation and flip ambiguity, which arises in the anchorless MDS algorithm. Knowledge of the displacement of the moving node is used to produce an analytical solution for the noise-free case. Subsequently, a least squares estimator is presented for the noisy scenario and the associated closed-form solution derived. The simulations show that the proposed algorithm accurately and efficiently estimates the locations of nodes, outperforming alternative methods.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-11-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-08-2019
Publisher: IEEE
Date: 05-2010
Publisher: Springer International Publishing
Date: 2020
Abstract: Localization in GNSS-denied/challenged indoor/outdoor and transitional environments represents a challenging research problem. As part of the joint IAG/FIG Working Groups 4.1.1 and 5.5 on Multi-sensor Systems, a benchmarking measurement c aign was conducted at The Ohio State University. Initial experiments have demonstrated that Cooperative Localization (CL) is extremely useful for positioning and navigation of platforms navigating in swarms or networks. In the data acquisition c aign, multiple sensor platforms, including vehicles, bicyclists and pedestrians were equipped with combinations of GNSS, Ultra-wide Band (UWB), Wireless Fidelity (Wi-Fi), Raspberry Pi units, cameras, Light Detection and Ranging (LiDAR) and inertial sensors for CL. Pedestrians wore a specially designed helmet equipped with some of these sensors. An overview of the experimental configurations, test scenarios, characteristics and sensor specifications is given. It has been demonstrated that all involved sensor platforms in the different test scenarios have gained a significant increase in positioning accuracy by using ubiquitous user localization. For ex le, in the indoor environment, success rates of approximately 97% were obtained using Wi-Fi fingerprinting for correctly detecting the room-level location of the user. Using UWB, decimeter-level positioning accuracy is demonstrable achievable under certain conditions. The full sets of data is being made available to the wider research community through the WG on request.
Publisher: Wiley
Date: 04-05-2022
DOI: 10.1111/TGIS.12939
Abstract: This article explores the design, implementation, and querying of a prototype system for automated spatial reasoning for geospatial intelligence applications, called NEXUS. The system combines multiple different reasoning components that can support a wide range of spatiotemporal queries. Fundamental to the requirements of intelligence analysts is the need to provide explanations of system outputs that help better inform users and engender trust in the system. The NEXUS architecture leverages semantic web technologies, and in particular the Simple Event Model and PROV‐O ontologies, to support queries not only about reasoner inferences, but also explanations as to why the system arrived at a particular conclusion. The manipulation of location reports in marine automatic identification system (AIS) data is used as a running ex le to demonstrate the approach. The range of queries developed illustrates both the detection of suspicious activities and explanations of the inference processes used to identify those activities.
Publisher: Elsevier BV
Date: 03-2019
Publisher: EDP Sciences
Date: 2019
DOI: 10.1051/E3SCONF/20199402001
Abstract: Increasingly, safety and liability critical applications require GNSS-like positioning metrics in environments where GNSS cannot work. Indoor navigation for the vision impaired and other mobility restricted in iduals, emergency responders and asset tracking in buildings demand levels of positioning accuracy and integrity that cannot be satisfied by current indoor positioning technologies and techniques. This paper presents the challenges facing positioning technologies for indoor positioning and presents innovative algorithms and approaches that aim to enhance performance in these difficult environments. The overall aim is to achieve GNSS-like performance in terms of autonomous, global, infrastructure free, portable and cost efficient. Preliminary results from a real-world experimental c aign conducted as part of the joint FIG Working Group 5.5 and IAG Sub-commission 4.1 on multi-sensor systems, demonstrate performance improvements based on differential Wi-Fi (DWi-Fi) and cooperative positioning techniques. The techniques, experimental schema and initial results will be fully documented in this paper.
Publisher: Cambridge University Press (CUP)
Date: 07-06-2011
DOI: 10.1017/S0373463311000075
Abstract: Cooperative positioning (CP) is a localization technique originally developed for use across wireless sensor networks. With the emergence of Dedicated Short Range Communications (DSRC) infrastructure for use in Intelligent Transportation Systems (ITS), CP techniques can now be adapted for use in location determination across vehicular networks. In vehicular networks, the technique of CP fuses GPS positions with additional sensed information such as inter-vehicle distances between the moving vehicles to determine their location within a neighbourhood. This paper presents the results obtained from a research study undertaken to demonstrate the capabilities of DSRC for meeting the positioning accuracies of road safety applications. The results show that a CP algorithm that fully integrates both measured/sensed data as well as navigation information such as map data can meet the positioning requirements of safety related applications of DSRC ( ·5 m). This paper presents the results of a Cramer Rao Lower Bound analysis which is used to benchmark the performance of the CP algorithm developed. The Kalman Filter (KF) models used in the CP algorithm are detailed and results obtained from integrating GPS positions, inter-vehicular ranges and information derived from in-vehicle maps are then discussed along with typical results as determined through a variety of network simulation studies.
Publisher: Springer International Publishing
Date: 2015
Publisher: Informa UK Limited
Date: 2004
Publisher: Informa UK Limited
Date: 03-07-2014
Publisher: Informa UK Limited
Date: 26-09-2016
Publisher: Springer Science and Business Media LLC
Date: 27-11-2018
Publisher: MDPI AG
Date: 04-12-2015
DOI: 10.3390/S151229821
Publisher: Springer Science and Business Media LLC
Date: 02-2003
Publisher: MDPI AG
Date: 08-08-2018
DOI: 10.3390/S18082602
Abstract: The extensive deployment of wireless infrastructure provides a low-cost way to track mobile users in indoor environment. This paper demonstrates a prototype model of an accurate and reliable room location awareness system in a real public environment in which three typical problems arise. Firstly, a massive number of access points (APs) can be sensed leading to a high-dimensional classification problem. Secondly, heterogeneous devices record different received signal strength (RSS) levels because of the variations in chip-set and antenna attenuation. Thirdly, APs are not necessarily visible in every scanning cycle leading to missing data issue. This paper presents a probabilistic Wi-Fi fingerprinting method in a hidden Markov model (HMM) framework for mobile user tracking. To account for spatial correlation of the signal strengths from multiple APs, a Multivariate Gaussian Mixture Model (MVGMM) was fitted to model the probability distribution of RSS measurements in each cell. Furthermore, the unseen property of invisible AP was investigated in this research, and demonstrated the efficiency as a beneficial information to differentiate between cells. The proposed system is able to achieve comparable localisation performance. Filed test results achieve a reliable 97% localisation room level accuracy of multiple mobile users in a real university c us Wi-Fi network.
Publisher: Computers, Materials and Continua (Tech Science Press)
Date: 2019
Publisher: Institute of Navigation
Date: 06-2021
DOI: 10.1002/NAVI.433
Publisher: American Society of Civil Engineers (ASCE)
Date: 11-2017
Publisher: Institute of Navigation
Date: 05-2019
DOI: 10.33012/2019.16857
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2013
Publisher: IGI Global
Date: 2012
DOI: 10.4018/978-1-4666-0209-0.CH012
Abstract: This chapter introduces the concept of Cooperative Positioning (CP) for vehicular networks, or more precisely, VANETs (Vehicular Adhoc NETworks), as an application of DSRC (Dedicated Short Range Communication). It includes a comprehensive review of available and hypothetical vehicular positioning technologies. Amongst these, the importance of CP for Location Based Services using DSRC is emphasized, and some important issues are addressed that need to be resolved in order to implement CP successfully with standard DSRC infrastructure. The performance bounds of CP are derived. Ranging between vehicles is identified as the main hurdle to be overcome. Time-based techniques of ranging are introduced, and the bandwidth requirements are investigated. The robustness of CP to inter-node connection failure as well as GPS (Global Positioning System) dropout is demonstrated via simulation. Kalman Filter performance for CP is evaluated, and proven to be efficient under conditions such as the consistency of GPS signal availability ranging between vehicles. CP has, however, shown to increase the positioning accuracy to 1-meter level, even in the deep urban valleys where vehicles frequently become invisible to navigation. Overall, CP is proven to be a viable concept and worthy of development as a DSRC application.
Publisher: Copernicus GmbH
Date: 02-06-2016
DOI: 10.5194/ISPRS-ANNALS-III-1-183-2016
Abstract: Abstract. Networks of small, low cost Unmanned Aerial Systems (UASs) have the potential to improve responsiveness and situational awareness across an increasing number of applications including defense, surveillance, mapping, search and rescue, disaster management, mineral exploration, assisted guidance and navigation etc. These ad hoc UAS networks typically have the capability to communicate with each other and can share data between the in idual UAS nodes. Thus these networks can operate as robust and efficient information acquisition platforms. For any of the applications involving UASs, a primary requirement is the localization i.e. determining the position and orientation of the UAS. The performance requirements of localization can vary with in idual applications, for ex le: mapping applications need much higher localization accuracy as compared to the applications involving only surveillance. The sharing of appropriate data between UASs can prove to be advantageous when compared to a single UAS, in terms of improving the positioning accuracy and reliability particularly in partially or completely GNSS denied environments. This research aims to integrate low cost positioning sensors and cooperative localization technique for a network of UASs. Our hypothesis is that it is possible to achieve high accurate, real-time localization of each of the nodes in the network even with cheaper sensors if the nodes of the network share information among themselves. This hypothesis is validated using simulations and the results are analyzed both for centralized and distributed estimation architectures. At first, the results are studied for a two node network which is then expanded for a network containing more number of nodes. Having more nodes in the network allows us to study the properties of the network including the effect of size and shape of the network on accuracy of the nodes.
Publisher: IOP Publishing
Date: 28-05-2021
Publisher: Informa UK Limited
Date: 12-2008
Publisher: MDPI AG
Date: 10-02-2021
DOI: 10.3390/ELECTRONICS10040435
Abstract: Absolute localization of a flying UAV on its own in a global-navigation-satellite-system (GNSS)-denied environment is always a challenge. In this paper, we present a landmark-based approach where a UAV is automatically locked into the landmark scene shown in a georeferenced image via a feedback control loop, which is driven by the output of an aerial image registration. To pursue a real-time application, we design and implement a speeded-up-robust-features (SURF)-based image registration algorithm that focuses efficiency and robustness under a 2D geometric transformation. A linear UAV controller with signals of four degrees of freedom is derived from the estimated transformation matrix. The approach is validated in a virtual simulation environment, with experimental results demonstrating the effectiveness and robustness of the proposed UAV self-localization system.
Publisher: MDPI AG
Date: 21-11-2022
DOI: 10.3390/S22229011
Abstract: Inertial attitude estimation is a crucial component of many modern systems and applications. Attitude estimation from commercial-grade inertial sensors has been the subject of an abundance of research in recent years due to the proliferation of Inertial Measurement Units (IMUs) in mobile devices, such as the smartphone. Traditional methodologies involve probabilistic, iterative-state estimation however, these approaches do not generalise well over changing motion dynamics and environmental conditions, as they require context-specific parameter tuning. In this work, we explore novel methods for attitude estimation from low-cost inertial sensors using a self-attention-based neural network, the Attformer. This paper proposes to part ways from the traditional cycle of continuous integration algorithms, and formulate it as an optimisation problem. This approach separates itself by leveraging attention operations to learn the complex patterns and dynamics associated with inertial data, allowing for the linear complexity in the dimension of the feature vector to account for these patterns. Additionally, we look at combining traditional state-of-the-art approaches with our self-attention method. These models were evaluated on entirely unseen sequences, over a range of different activities, users and devices, and compared with a recent alternate deep learning approach, the unscented Kalman filter and the iOS CoreMotion API. The inbuilt iOS had a mean angular distance from the true attitude of 117.31∘, the GRU 21.90∘, the UKF 16.38∘, the Attformer 16.28∘ and, finally, the UKF-Attformer had mean angular distance of 10.86∘. We show that this plug-and-play solution outperforms previous approaches and generalises well across different users, devices and activities.
Publisher: Informa UK Limited
Date: 06-09-2013
Publisher: Cambridge University Press (CUP)
Date: 26-04-2022
DOI: 10.1017/S0373463322000194
Abstract: Indoor/Outdoor (IO) detection (IOD) using Wi-Fi- and smartphone-based technologies is in high demand and interest in both the industrial and research fields. This paper proposes a novel and effective hybrid IOD (HIOD) approach for detecting a smartphone user's IO location. The HIOD approach uses signals received from both Wi-Fi and GPS as well as the latest positioning technologies such as multilateration, fingerprinting and machine learning. This paper proposes and implements two-level signal-to-noise ratio (SNR) threshold parameters for the first time, which are specifically derived from GPS signals through 42 empirical tests at seven test sites with adequate environmental factors considered. Using the newly derived IOD threshold parameters and a set of IO detection rules, the HIOD approach is then tested at 20 test points (TPs) in a city canyon area, where most of the TPs are under semi-indoor or semi-outdoor conditions. The final test results show that a 100% IOD rate is achieved.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: MDPI AG
Date: 17-03-2023
DOI: 10.3390/S23063217
Abstract: Inertial localisation is an important technique as it enables ego-motion estimation in conditions where external observers are unavailable. However, low-cost inertial sensors are inherently corrupted by bias and noise, which lead to unbound errors, making straight integration for position intractable. Traditional mathematical approaches are reliant on prior system knowledge, geometric theories and are constrained by predefined dynamics. Recent advances in deep learning, which benefit from ever-increasing volumes of data and computational power, allow for data-driven solutions that offer more comprehensive understanding. Existing deep inertial odometry solutions rely on estimating the latent states, such as velocity, or are dependent on fixed-sensor positions and periodic motion patterns. In this work, we propose taking the traditional state estimation recursive methodology and applying it in the deep learning domain. Our approach, which incorporates the true position priors in the training process, is trained on inertial measurements and ground truth displacement data, allowing recursion and learning both motion characteristics and systemic error bias and drift. We present two end-to-end frameworks for pose invariant deep inertial odometry that utilises self-attention to capture both spatial features and long-range dependencies in inertial data. We evaluate our approaches against a custom 2-layer Gated Recurrent Unit, trained in the same manner on the same data, and tested each approach on a number of different users, devices and activities. Each network had a sequence length weighted relative trajectory error mean ≤0.4594 m, highlighting the effectiveness of our learning process used in the development of the models.
Publisher: Informa UK Limited
Date: 24-04-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-10-2019
Publisher: Cambridge University Press (CUP)
Date: 15-12-2005
DOI: 10.1017/S0373463305003504
Abstract: Recently new location technologies have emerged that can be employed in modern advanced navigation systems. They can be employed to augment Global Navigation Satellite System (GNSS) positioning techniques and dead reckoning as they offer different levels of positioning accuracies and performance. An integration of other technologies is especially required in indoor and outdoor-to-indoor environments. The paper gives an overview of the newly developed ubiquitous positioning technologies and their integration in navigation systems. Furthermore two case studies are presented, i.e., the improvement of land vehicle safety using Augmented Reality (AR) technologies and pedestrian navigation services for the guidance of users to certain University offices. In the first case study the integration of map matching into a Kalman filter approach is performed (referred to as “Intelligent Vehicle Navigation”) and its principle is briefly described. This approach can also be adapted for the pedestrian navigation service described in the second case study.
Publisher: MDPI AG
Date: 25-08-2020
Abstract: Wooden power poles and their ongoing inspection represent a significant investment for most electrical power utilities. This study explored the potential for using microwave fields to non-invasively assess the state of hardwood power poles in a field experiment. Two strategies were assessed: 2.4 GHz microwave field transmission through the pole and mutual coupling between antennae using a 10.525 GHz radar module applied to the surface of the pole. Both systems distinguished between sound hardwood poles and those which were compromised by decay and subterranean termite attack and infestation.
Publisher: IEEE
Date: 04-2020
Publisher: Copernicus GmbH
Date: 19-06-2018
Abstract: Abstract. The determination of the distribution of water vapor in the atmosphere plays an important role in the atmospheric monitoring. Global Navigation Satellite Systems (GNSS) tomography can be used to construct 3-D distribution of water vapor over the field covered by a GNSS network with high temporal and spatial resolutions. In current tomographic approaches, a pre-set fixed rectangular field that roughly covers the area of the distribution of the GNSS signals on the top plane of the tomographic field is commonly used for all tomographic epochs. Due to too many unknown parameters needing to be estimated, the accuracy of the tomographic solution degrades. Another issue of these approaches is their unsuitability for GNSS networks with a low number of stations, as the shape of the field covered by the GNSS signals is, in fact, roughly that of an upside-down cone rather than the rectangular cube as the pre-set. In this study, a new approach for determination of tomographic fields fitting the real distribution of GNSS signals on different tomographic planes at different tomographic epochs and also for discretization of the tomographic fields based on the perimeter of the tomographic boundary on the plane and meshing techniques is proposed. The new approach was tested using three stations from the Hong Kong GNSS network and validated by comparing the tomographic results against radiosonde data from King's Park Meteorological Station (HKKP) during the one month period of May 2015. Results indicated that the new approach is feasible for a three-station GNSS network tomography. This is significant due to the fact that the conventional approaches cannot even solve a network tomography from a few stations.
Publisher: IEEE
Date: 04-2015
Publisher: Walter de Gruyter GmbH
Date: 12-02-2020
Abstract: Localization in GNSS-denied/challenged indoor/outdoor and transitional environments represents a challenging research problem. This paper reports about a sequence of extensive experiments, conducted at The Ohio State University (OSU) as part of the joint effort of the FIG/IAG WG on Multi-sensor Systems. Their overall aim is to assess the feasibility of achieving GNSS-like performance for ubiquitous positioning in terms of autonomous, global, preferably infrastructure-free positioning of portable platforms at affordable cost efficiency. In the data acquisition c aign, multiple sensor platforms, including vehicles, bicyclists and pedestrians were used whereby cooperative positioning (CP) is the major focus to achieve a joint navigation solution. The GPSVan of The Ohio State University was used as the main reference vehicle and for pedestrians, a specially designed helmet was developed. The employed/tested positioning techniques are based on using sensor data from GNSS, Ultra-wide Band (UWB), Wireless Fidelity (Wi-Fi), vison-based positioning with cameras and Light Detection and Ranging (LiDAR) as well as inertial sensors. The experimental and initial results include the preliminary data processing, UWB sensor calibration and Wi-Fi indoor positioning with room-level granularity and platform trajectory determination. The results demonstrate that CP techniques are extremely useful for positioning of platforms navigating in swarms or networks. A significant performance improvement in terms of positioning accuracy and reliability is achieved. Using UWB, decimeter-level positioning accuracy is achievable under typical conditions, such as normal walls, average complexity buildings, etc. Using Wi-Fi fingerprinting, success rates of approximately 97 % were obtained for correctly detecting the room-level location of the user.
Publisher: Elsevier BV
Date: 03-2012
Publisher: Springer International Publishing
Date: 2017
Publisher: Wiley
Date: 04-2008
Publisher: Wiley
Date: 03-2006
Publisher: Elsevier
Date: 2019
Publisher: IEEE
Date: 10-2014
Publisher: MDPI AG
Date: 29-11-2019
DOI: 10.3390/S19235274
Abstract: Cooperative positioning (CP) utilises information sharing among multiple nodes to enable positioning in Global Navigation Satellite System (GNSS)-denied environments. This paper reports the performance of a CP system for pedestrians using Ultra-Wide Band (UWB) technology inGNSS-denied environments. This data set was collected as part of a benchmarking measurementc aign carried out at the Ohio State University in October 2017. Pedestrians were equippedwith a variety of sensors, including two different UWB systems, on a specially designed helmetserving as a mobile multi-sensor platform for CP. Different users were walking in stop-and-go modealong trajectories with predefined checkpoints and under various challenging environments. Inthe developed CP network, both Peer-to-Infrastructure (P2I) and Peer-to-Peer (P2P) measurementsare used for positioning of the pedestrians. It is realised that the proposed system can achievedecimetre-level accuracies (on average, around 20 cm) in the complete absence of GNSS signals,provided that the measurements from infrastructure nodes are available and the network geometryis good. In the absence of these good conditions, the results show that the average accuracydegrades to meter level. Further, it is experimentally demonstrated that inclusion of P2P cooperativerange observations further enhances the positioning accuracy and, in extreme cases when only oneinfrastructure measurement is available, P2P CP may reduce positioning errors by up to 95%. Thecomplete test setup, the methodology for development, and data collection are discussed in thispaper. In the next version of this system, additional observations such as theWi-Fi, camera, and othersignals of opportunity will be included.
Publisher: Institute of Navigation
Date: 19-02-2019
DOI: 10.33012/2019.16726
Publisher: IEEE
Date: 03-2013
Publisher: Informa UK Limited
Date: 09-2013
Publisher: Informa UK Limited
Date: 03-12-2020
Publisher: Copernicus GmbH
Date: 06-08-2020
DOI: 10.5194/ISPRS-ARCHIVES-XLIII-B1-2020-549-2020
Abstract: Abstract. The increasing demand for reliable indoor navigation systems is leading the research community to investigate various approaches to obtain effective solutions usable with mobile devices. Among the recently proposed strategies, Ultra-Wide Band (UWB) positioning systems are worth to be mentioned because of their good performance in a wide range of operating conditions. However, such performance can be significantly degraded by large UWB range errors mostly, due to non-line-of-sight (NLOS) measurements. This paper considers the integration of UWB with vision to support navigation and mapping applications. In particular, this work compares positioning results obtained with a simultaneous localization and mapping (SLAM) algorithm, exploiting a standard and a Time-of-Flight (ToF) camera, with those obtained with UWB, and then with the integration of UWB and vision. For the latter, a deep learning-based recognition approach was developed to detect UWB devices in camera frames. Such information is both introduced in the navigation algorithm and used to detect NLOS UWB measurements. The integration of this information allowed a 20% positioning error reduction in this case study.
Publisher: Copernicus GmbH
Date: 08-06-2016
DOI: 10.5194/ISPRSARCHIVES-XLI-B2-509-2016
Abstract: Indoor localization is important for a variety of applications such as location-based services, mobile social networks, and emergency response. Fusing spatial information is an effective way to achieve accurate indoor localization with little or with no need for extra hardware. However, existing indoor localization methods that make use of spatial information are either too computationally expensive or too sensitive to the completeness of landmark detection. In this paper, we solve this problem by using the proposed landmark graph. The landmark graph is a directed graph where nodes are landmarks (e.g., doors, staircases, and turns) and edges are accessible paths with heading information. We compared the proposed method with two common Dead Reckoning (DR)-based methods (namely, Compass + Accelerometer + Landmarks and Gyroscope + Accelerometer + Landmarks) by a series of experiments. Experimental results show that the proposed method can achieve 73% accuracy with a positioning error less than 2.5 meters, which outperforms the other two DR-based methods.
Publisher: Informa UK Limited
Date: 12-2002
Publisher: Informa UK Limited
Date: 2008
Publisher: CRC Press
Date: 19-04-2016
DOI: 10.1201/B14940-2
Publisher: MDPI AG
Date: 30-11-2021
DOI: 10.3390/RS13234858
Abstract: The availability of global navigation satellite systems (GNSS) on consumer devices has caused a dramatic change in every-day life and human behaviour globally. Although GNSS generally performs well outdoors, unavailability, intentional and unintentional threats, and reliability issues still remain. This has motivated the deployment of other complementary sensors in such a way that enables reliable positioning, even in GNSS-challenged environments. Besides sensor integration on a single platform to remedy the lack of GNSS, data sharing between platforms, such as in collaborative positioning, offers further performance improvements for positioning. An essential element of this approach is the availability of internode measurements, which brings in the strength of a geometric network. There are many sensors that can support ranging between platforms, such as LiDAR, camera, radar, and many RF technologies, including UWB, LoRA, 5G, etc. In this paper, to demonstrate the potential of the collaborative positioning technique, we use ultra-wide band (UWB) transceivers and vision data to compensate for the unavailability of GNSS in a terrestrial vehicle urban scenario. In particular, a cooperative positioning approach exploiting both vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) UWB measurements have been developed and tested in an experiment involving four cars. The results show that UWB ranging can be effectively used to determine distances between vehicles (at sub-meter level), and their relative positions, especially when vision data or a sufficient number of V2V ranges are available. The presence of NLOS observations is one of the principal factors causing a decrease in the UWB ranging performance, but modern machine learning tools have shown to be effective in partially eliminating NLOS observations. According to the obtained results, UWB V2I can achieve sub-meter level of accuracy in 2D positioning when GNSS is not available. Combining UWB V2I and GNSS as well V2V ranging may lead to similar results in cooperative positioning. Absolute cooperative positioning of a group of vehicles requires stable V2V ranging and that a certain number of vehicles in the group are provided with V2I ranging data. Results show that meter-level accuracy is achieved when at least two vehicles in the network have V2I data or reliable GNSS measurements, and usually when vehicles lack V2I data but receive V2V ranging to 2–3 vehicles. These working conditions typically ensure the robustness of the solution against undefined rotations. The integration of UWB with vision led to relative positioning results at sub-meter level of accuracy, an improvement of the absolute positioning cooperative results, and a reduction in the number of vehicles required to be provided with V2I or GNSS data to one.
Publisher: Optica Publishing Group
Date: 29-03-2022
DOI: 10.1364/OE.454412
Abstract: The fundamental understanding of biological pathways requires minimally invasive nanoscopic optical resolution imaging. Many approaches to high-resolution imaging rely on localization of single emitters, such as fluorescent molecules or quantum dots. Additionally, the exact determination of the number of such emitters in an imaging volume is essential for a number of applications however, in standard intensity-based microscopy it is not possible to determine the number of in idual emitters within a diffraction limited spot without initial knowledge of system parameters. Here we explore how quantum measurements of the emitted photons using photon number resolving detectors can be used to address this challenging task. In the proposed new approach, the problem of counting emitters reduces to the task of determining differences between the emitted photon distribution and the Poisson limit. We show that quantum measurements of the number of photons emitted from an ensemble of emitters enable the determination of both the number of emitters and the probability of emission. This method can be applied for any type of single-photon emitters. The scaling laws of this new approach are presented by the Cramer-Rao Lower Bounds, and this technique has great potential in quantum optical imaging with nanoscopic resolution.
Publisher: MDPI AG
Date: 20-09-2018
DOI: 10.3390/JSAN7040042
Abstract: Precise localization is one of the key requirements in the deployment of UAVs (Unmanned Aerial Vehicles) for any application including precision mapping, surveillance, assisted navigation, search and rescue. The need for precise positioning is even more relevant with the increasing automation in UAVs and growing interest in commercial UAV applications such as transport and delivery. In the near future, the airspace is expected to be occupied with a large number of unmanned as well as manned aircraft, a majority of which are expected to be operating autonomously. This paper develops a new cooperative localization prototype that utilizes information sharing among UAVs and static anchor nodes for precise positioning of the UAVs. The UAVs are retrofitted with low-cost sensors including a camera, GPS receiver, UWB (Ultra Wide Band) radio and low-cost inertial sensors. The performance of the low-cost prototype is evaluated in real-world conditions in partially and obscured GNSS (Global Navigation Satellite Systems) environments. The performance is analyzed for both centralized and distributed cooperative network designs. It is demonstrated that the developed system is capable of achieving navigation grade (2–4 m) accuracy in partially GNSS denied environments, provided a consistent communication in the cooperative network is available. Furthermore, this paper provides experimental validation that information sharing is beneficial to improve positioning performance even in ideal GNSS environments. The experiments demonstrate that the major challenges for low-cost cooperative networks are consistent connectivity among UAV platforms and sensor synchronization.
Publisher: Informa UK Limited
Date: 12-2007
Publisher: Walter de Gruyter GmbH
Date: 2012
Publisher: Cambridge University Press (CUP)
Date: 05-2002
DOI: 10.1017/S0373463302001753
Abstract: The increasing availability of small, low-cost GPS receivers has established a firm growth in the production of Location-Based Services (LBS). LBS, such as in-car navigation systems, are not necessarily reliant on high accuracy but a continuous positioning service. When available, the accuracy provided by the standard positioning service (SPS) of 30 metres, 95% of the time is often acceptable. The reality is, however, that GPS does not work in all situations, and it is therefore common to integrate GPS with additional sensors. The use of low-cost inertial sensors alone during GPS signal outage is severely restricted due to the accumulation of errors that is inherent with such dead reckoning (DR) systems. Through the integration of spatial information with real-time positioning sensors, intelligence can be added to the land mobile navigation solution. The information contained within a Geographical Information System (GIS) provides additional observations that can be used to improve the navigation result. With this approach, the solution is not dependent on the performance capabilities of the navigation sensors alone. This enables the use of lower accuracy navigation devices, allowing low-cost systems to provide a sustained, viable navigation solution despite long-term GPS outages. Practical results are presented comparing solutions obtained from a hand-held GPS receiver to a gyroscope and odometer.
Publisher: Elsevier BV
Date: 05-2016
Publisher: IEEE
Date: 11-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2016
Publisher: MDPI AG
Date: 09-11-2018
DOI: 10.3390/S18113860
Abstract: The erse operating environments change GNSS measurement noise covariance in real time, and different GNSS techniques hold different measurement noise covariance as well. Mismodelling the covariance causes undependable filtering results and even degenerates the GNSS/INS Particle Filter (PF) process, due to the fact that INS error-state noise covariance is much smaller than that of GNSS measurement noise. It also makes the majority of existing methods for adaptively adjusting filter parameters incapable of performing well. In this paper, a feasible Digital Track Map-aided (DTM-aided) adaptive extended Kalman particle filter method is introduced in GNSS/INS integration in order to adjust GNSS measurement noise covariance in real time, and the GNSS down-direction offset is also estimated along with every s ling period through making full use of DTM information. The proposed approach is successfully examined in a railway environment, and the on-site experimental results reveal that the adaptive approach holds better positioning performance in comparison to the methods without adaptive adjustment. Improvements of 62.4% and 14.9% in positioning accuracy are obtained in contrast to Standard Point Positioning (SPP) and Precise Point Positioning (PPP), respectively. The proposed adaptive method takes advantage of DTM information and is able to automatically adapt to complex railway environments and different GNSS techniques.
Publisher: Cambridge University Press (CUP)
Date: 12-03-2012
DOI: 10.1017/S0373463311000610
Abstract: Vehicular communication technologies are becoming staples of modern societies. This paper proposes a new positioning algorithm for vehicular networks. The algorithm is a non-classic Multi-Dimensional Scaling Filter (MDSF) that builds on a novel and computationally effective Multi-Dimensional Scaling (MDS) solution covariance estimation technique and also a Maximum Likelihood (ML) filter. In general a major drawback of the non-classic MDS is the high computational cost because of its iterative nature. It is shown that a special blend between vehicular Map-Matching (MM) and MDSF considerably reduces the number of iterations and the convergence time, making the MDSF a suitable algorithm for vehicular network positioning. The performance of MDSF is compared with that of an Extended Kalman Filter (EKF) together with the Cramar Rao Lower Bound (CRLB). It is shown through simulation that for all types of traffic conditions MDSF performs better than EKF and closer to CRLB than EKF. It is also shown that both MDSF and EKF algorithms are robust to typical Global Positioning System (GPS) outages in deep urban canyons. CRLB also proves that Cooperative Positioning (CP) in general has the ability to bridge short GPS outages.
Publisher: Informa UK Limited
Date: 20-01-2016
Publisher: Springer Science and Business Media LLC
Date: 15-01-2019
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Informa UK Limited
Date: 06-08-2023
Publisher: Walter de Gruyter GmbH
Date: 2015
Abstract: PNT stands for Positioning, Navigation, and Timing. Space-based PNT refers to the capabilities enabled by GNSS, and enhanced by Ground and Space-based Augmentation Systems (GBAS and SBAS), which provide position, velocity, and timing information to an unlimited number of users around the world, allowing every user to operate in the same reference system and timing standard. Such information has become increasingly critical to the security, safety, prosperity, and overall qualityof-life of many citizens. As a result, space-based PNT is now widely recognized as an essential element of the global information infrastructure. This paper discusses the importance of the availability and continuity of PNT information, whose application, scope and significance have exploded in the past 10–15 years. A paradigm shift in the navigation solution has been observed in recent years. It has been manifested by an evolution from traditional single sensor-based solutions, to multiple sensor-based solutions and ultimately to collaborative navigation and layered sensing, using non-traditional sensors and techniques – so called signals of opportunity. A joint working group under the auspices of the International Federation of Surveyors (FIG) and the International Association of Geodesy (IAG), entitled ‘Ubiquitous Positioning Systems’ investigated the use of Collaborative Positioning (CP) through several field trials over the past four years. In this paper, the concept of CP is discussed in detail and selected results of these experiments are presented. It is demonstrated here, that CP is a viable solution if a ‘network’ or ‘neighbourhood’ of users is to be positioned / navigated together, as it increases the accuracy, integrity, availability, and continuity of the PNT information for all users.
Publisher: MDPI AG
Date: 26-10-2015
DOI: 10.3390/S151027251
Publisher: Institute of Navigation
Date: 03-11-2017
DOI: 10.33012/2017.15160
Publisher: Hindawi Limited
Date: 2018
DOI: 10.1155/2018/8035093
Abstract: As more spacecraft are launched into the Geostationary Earth Orbit (GEO) belt, the possibility of fatal collisions or unnecessary interference between spacecraft increases. In this paper, a new location-awareness method that uses CubeSats is proposed to assist with radiofrequency (RF) domain verification by means of awareness and identification of the positions of the spot beam emitters of communications satellites in geostationary orbit. By flying a CubeSat (or a constellation of CubeSats) through the coverage area of a spot beam, the spot beam emitter’s position is identified and the spot beam’s pattern knowledge is characterized. The geometry, the equations of motion of the spacecraft, the measurement process, and the filtering equations in a location system are addressed with respect to the location methods investigated in this study. A realistic scenario in which a CubeSat receives signals from GEO communications satellites is simulated using the Systems Tool Kit (STK). The results of the simulation and the analysis presented in this study provide a thorough verification of the performance of the location-awareness method.
Publisher: Copernicus GmbH
Date: 14-09-2017
DOI: 10.5194/ISPRS-ANNALS-IV-2-W4-401-2017
Abstract: Abstract. 3D models of indoor environments are essential for many application domains such as navigation guidance, emergency management and a range of indoor location-based services. The principal components defined in different BIM standards contain not only building elements, such as floors, walls and doors, but also navigable spaces and their topological relations, which are essential for path planning and navigation. We present an approach to automatically reconstruct topological relations between navigable spaces from point clouds. Three types of topological relations, namely containment, adjacency and connectivity of the spaces are modelled. The results of initial experiments demonstrate the potential of the method in supporting indoor navigation.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2013
Publisher: IEEE
Date: 10-2014
Publisher: American Society of Civil Engineers (ASCE)
Date: 2019
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Allison Kealy.