ORCID Profile
0000-0001-5472-2250
Current Organisations
UNSW Sydney
,
Nanjing University of Aeronautics and Astronautics
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Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: MDPI AG
Date: 20-04-2022
DOI: 10.3390/RS14091976
Abstract: As navigation is a key to task execution of micro unmanned aerial vehicle (UAV) swarm, the cooperative navigation (CN) method that integrates relative measurements between UAVs has attracted widespread attention due to its performance advantages. In view of the precision and efficiency of cooperative navigation for low-cost micro UAV swarm, this paper proposes a sigma point belief propagation-based (SPBP) CN method that can integrate self-measurement data and inter-UAV ranging in a distributed manner so as to improve the absolute positioning performance of UAV swarm. The method ides the sigma point filter into two steps: the first is to integrate local measurement data the second is to approximate the belief of position based on the mean and covariance of the state, and pass message by augmentation, res ling and cooperative measurement update of the state to realize a low-complexity approximation to traditional message passing method. The simulation results and outdoor flight test results show that with similar performance, the proposed CN method has a calculation load more than 20 times less than traditional BP algorithms.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2021
Publisher: SAGE Publications
Date: 11-2020
Abstract: A fault-detection method for relative navigation based on Kullback–Leibler ergence (KLD) is proposed. Different from the traditional χ 2 -based approaches, the KLD for a filter is following a hybrid distribution that combines χ 2 distribution and F-distribution. Using extended Kalman filter (EKF) as the estimator, the distance between the priori and posteriori data of EKF is calculated to detect the abnormal measurements. After fault detection step, a fault exclusion method is applied to remove the error observations from the fusion procedure. The proposed method is suitable for the Kalman filter-based multisensor relative navigation system. Simulation and experimental results show that the proposed method can detect the abnormal measurement successfully, and its positioning accuracy after fault detection and exclusion outperforms the traditional χ 2 -based method.
Publisher: SAGE Publications
Date: 2022
DOI: 10.1177/15501477211064758
Abstract: Navigation plays an important role in the task execution of the micro-unmanned aerial vehicle (UAV) swarm. The Cooperative Navigation (CN) method that fuses the observation of onboard sensors and relative information between UAVs is a research hotspot. Aiming at the efficiency and accuracy problems of previous studies, this article proposes a hybrid-CN method for UAV swarm based on Factor Graph and Kalman filter. A global Factor Graph is used to combine Global Navigation Satellite System (GNSS) and ranging information to provide position estimations for modifying the distributed Kalman filter distributed Kalman filter is established on each UAV to fuse inertial information and optimized position estimation to modify the navigation states. In order to provide time-consistent GNSS position information for the Factor Graph, a time synchronization filter is designed. The proposed method is tested and verified using standard Monte Carlo simulations, simulation results show that it can provide a more precise and efficient CN solution than traditional CN methods.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
No related grants have been discovered for Jun Xiong.