Constrained MEMS-Based INS/UWB Tightly Coupled System for Accurate UGVs Navigation

To enhance the navigation performance and robustness of navigation system combining ultrawideband (UWB) and inertial navigation systems (INS) under complex indoor environments, an improved navigation method—Allan variance (AV) to assist a modified adaptive extended Kalman Filter based on the dynamic...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2023-05, Vol.15 (10), p.2535
Main Authors: Mi, Jing, Wang, Qing, Han, Xiaotao
Format: Article
Language:English
Subjects:
Accuracy
adaptive extended Kalman filter (AEKF)
Algorithms
Allan variance (AV)
Bias
Electronics in navigation
Extended Kalman filter
Field tests
Indoor environments
Inertial navigation
Inertial navigation (Aeronautics)
inertial navigation system (INS)
Inertial sensing devices
Kalman filters
Microelectromechanical systems
navigation performance
Navigation systems
Noise measurement
Remote sensing
Robustness
Sensors
Stochasticity
Ultrawideband
ultrawideband (UWB)
Velocity
Weight
Weighting functions
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Summary:To enhance the navigation performance and robustness of navigation system combining ultrawideband (UWB) and inertial navigation systems (INS) under complex indoor environments, an improved navigation method—Allan variance (AV) to assist a modified adaptive extended Kalman Filter based on the dynamic weight function (DWF-MAEFF)—is proposed. Firstly, AV is used to improved INS error dynamics by modeling the stochastic noise of an inertial sensor; which can compensate for inertial sensor error caused by stochastic noise during integrated navigation. Secondly, the MAEKF is developed by designing the weight function to adjust the weight of measurement noise reasonably and dynamically, which can further improve the robustness of the AEKF algorithm. Field tests were conducted to verify the effectiveness of the proposed navigation method. The result indicated that an improvement of up to 60% over the existing integrated navigation method based on EKF and AEKF can be obtained by the proposed method.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs15102535