Integrated navigation for doppler velocity log aided strapdown inertial navigation system based on robust IMM algorithm

Abstract Global navigation satellite system (GNSS) is the most widely used position, navigation and time service system. However, GNSS such as BeiDou Navigation Satellite System (BDS) cannot provide the external assistance information for the autonomous underwater vehicle (AUV) because of the blocking effect of water on electromagnetic wave. The uncertain and non-Gaussian characteristics of the measurement noise may degrade the performance of SINS/DVL integrated navigation under the standard Kalman filter (KF) framework. To solve this problem, this paper proposes a robust interacting multiple model algorithm (IMM-RKF) based on the Mahalanobis distance algorithm. Furthermore, two switching sub-models will depict the uncertain parameters. The contributions of the work presented here are twofold. First, this paper robustifies the IMM algorithm under non Gaussian condition, and second the research method is verified in the problem of SINS/DVL integrated navigation. Simulation experimental results for the problem of SINS/DVL integrated navigation under non-Gaussian and uncertain conditions demonstrate the validity and superiority of the proposed method.

[1]  V. Vaidehi,et al.  IMM-based Kalman filter for channel estimation in UWB OFDM systems , 2007 .

[2]  Y. Bar-Shalom,et al.  The interacting multiple model algorithm for systems with Markovian switching coefficients , 1988 .

[3]  Wanli Li,et al.  A Novel Scheme for DVL-Aided SINS In-Motion Alignment Using UKF Techniques , 2013, Sensors.

[4]  P. Rousseeuw,et al.  Unmasking Multivariate Outliers and Leverage Points , 1990 .

[5]  Mike Tooley,et al.  Aircraft Communications and Navigation Systems , 2017 .

[6]  Baiqing Hu,et al.  Huber-based novel robust unscented Kalman filter , 2012 .

[7]  Bing Zhu,et al.  Huber-Based Adaptive Unscented Kalman Filter with Non-Gaussian Measurement Noise , 2018, Circuits Syst. Signal Process..

[8]  Guobin Chang,et al.  An adaptive fading Kalman filter based on Mahalanobis distance , 2015 .

[9]  Lubin Chang,et al.  Initial Alignment for a Doppler Velocity Log-Aided Strapdown Inertial Navigation System With Limited Information , 2017, IEEE/ASME Transactions on Mechatronics.

[10]  Jacek Malec,et al.  Underwater Terrain Navigation Using Standard Sea Charts and Magnetic Field Maps , 2017, 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI).

[11]  G. Chang.  Robust Kalman filtering based on Mahalanobis distance as outlier judging criterion , 2014, Journal of Geodesy.

[12]  William Dale Blair,et al.  Interacting multiple bias model algorithm with application to tracking maneuvering targets , 1992, [1992] Proceedings of the 31st IEEE Conference on Decision and Control.

[13]  Bing Zhu,et al.  Robust adaptive unscented Kalman filter and its application in initial alignment for body frame velocity aided strapdown inertial navigation system. , 2018, The Review of scientific instruments.

[14]  Yonggang Zhang,et al.  A Novel Robust Student's t-Based Kalman Filter , 2017, IEEE Transactions on Aerospace and Electronic Systems.

[15]  Xu De-min,et al.  Application of simplified UKF in SINS initial alignment for large misalignment angles , 2008 .