UAV Swarm Navigation Using Dynamic Adaptive Kalman Filter and Network Navigation

Aiming to improve the positioning accuracy of an unmanned aerial vehicle (UAV) swarm under different scenarios, a two-case navigation scheme is proposed and simulated. First, when the Global Navigation Satellite System (GNSS) is available, the inertial navigation system (INS)/GNSS-integrated system...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-08, Vol.21 (16), p.5374
Main Authors: Zhang, Jingjuan, Zhou, Wenxiang, Wang, Xueyun
Format: Article
Language:English
Subjects:
Accuracy
adaptive Kalman filter
Algorithms
Covariance matrix
ensemble empirical mode decomposition
Inertial navigation
Kalman filters
Navigation systems
networked navigation
Neural networks
Noise
observation noise
Position errors
process noise
Sensors
Signal processing
Simulation
Stochastic models
Unmanned aerial vehicles
Wavelet transforms
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Summary:Aiming to improve the positioning accuracy of an unmanned aerial vehicle (UAV) swarm under different scenarios, a two-case navigation scheme is proposed and simulated. First, when the Global Navigation Satellite System (GNSS) is available, the inertial navigation system (INS)/GNSS-integrated system based on the Kalman Filter (KF) plays a key role for each UAV in accurate navigation. Considering that Kalman filter’s process noise covariance matrix Q and observation noise covariance matrix R affect the navigation accuracy, this paper proposes a dynamic adaptive Kalman filter (DAKF) which introduces ensemble empirical mode decomposition (EEMD) to determine R and adjust Q adaptively, avoiding the degradation and divergence caused by an unknown or inaccurate noise model. Second, a network navigation algorithm (NNA) is employed when GNSS outages happen and the INS/GNSS-integrated system is not available. Distance information among all UAVs in the swarm is adopted to compensate the INS position errors. Finally, simulations are conducted to validate the effectiveness of the proposed method, results showing that DAKF improves the positioning accuracy of a single UAV by 30–50%, and NNA increases the positioning accuracy of a swarm by 93%.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21165374