一种故障检测与抗差估计协同的AUV容错导航算法OA

In the integrated navigation system of autonomous undersea vehicles(AUVs),traditional adaptive filtering algorithms struggle to achieve effective fault tolerance against slowly varying faults of the Doppler velocity log(DVL)due to the conflict between noise estimation and fault detection mechanisms.To address this issue,this paper proposed a collaborative fault-tolerant navigation method fusing a long short-term memory(LSTM)network-based fault detection with a variational Bayesian adaptive Kalman filter(VBAKF)and IGG-III robust filtering.The proposed method utilized the LSTM network to effectively identify the early characteristics of slowly varying faults.Upon fault confirmation,the filter switched from the VBAKF to the IGG-III robust filtering mode and dynamically constructed equivalent weight matrices to suppress the influence of faulty measurements.After the fault ended,the VBAKF was restored to maintain optimal estimation.Experimental results demonstrate that in the event of DVL slowly varying faults,the proposed method achieves higher navigation accuracy than several mainstream filtering algorithms,effectively suppresses state estimation distortion,and enhances the positioning precision and robustness of the integrated navigation system of AUVs in uncertain underwater environments.