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Robust Interactive Multimodel INS/DVL Intergrated Navigation System With Adaptive Model Set
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AI
DOI：10.1109/JSEN.2023.3252177.png)
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Abstract
En 中文
A robust interactive multiple model (RIMM) algorithm with the adaptive model set is proposed to improve the performance of inertial navigation system (INS) and Doppler velocity log (DVL) integrated navigation system under a complex measurement environment with undesirable heavy-tailed non-Gaussian noise. Specifically, an improved Huber kernel function is applied to the robust error state Kalman filter (ESKF) for its benefit of better resisting larger measurement outliers. In addition, a flexible adaptive model set update strategy is proposed where the model set is determined by the current and historical measurement information stored in the sliding window. Also, for the model set update, the main model is no longer fixed, and it will be determined based on the probability weights corresponding to each model. This innovative RIMM algorithm is compared with ESKF, interactive multiple model (IMM), and hybrid IMM (HIMM) through simulations, autonomous underwater vehicle (AUV) lake trial, and semiphysical simulations. Experimental results show that our proposed algorithm has outstanding accuracy and robustness under a heavy-tailed non-Gaussian noise environment.
Keywords:
Navigation
Mathematical models
Adaptation models
Heavily-tailed distribution
Sea measurements
Sensors
Noise measurement
Adaptive filters
error state Kalman filter (ESKF)
heavy-tailed non-Gaussian noise
inertial navigation system (INS)/Doppler velocity log (DVL) integrated navigation system