SINS/DVL integrated based on interacting multiple model approach by integrating feedback particle filter for ship navigation

Chengcheng Liang; Chuang Zhang; Chen Guo

doi:10.1117/12.2640749

13 October 2022 SINS/DVL integrated based on interacting multiple model approach by integrating feedback particle filter for ship navigation

Chengcheng Liang, Chuang Zhang, Chen Guo

Author Affiliations +

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 122872J (2022) https://doi.org/10.1117/12.2640749
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

Abstract

To improve the performance of strapdown inertial navigation system (SINS)/ Doppler velocity logs (DVL) integrated navigation system, a new Interacting Multiple Model Feedback Particle Filter (IMMFPF) approach is proposed under the condition of complex and changeable underwater environment. In this scheme, the Interacting Multiple Model uses to deal with the model switch, while FPF approach takes each particle as a controllable stochastic system. By finding the solution about the Euler-Lagrange boundary value, the optimal feedback control law of Kullback-Leibler distance is obtained, and then the state of the particle itself is corrected and updated. Both simulations and field test illustrate that proposed IMMFPF approach can achieve a more accurate solution and make a more correct and effective response for SINS/DVL integrated navigation system.

Citation Download Citation

Chengcheng Liang, Chuang Zhang, and Chen Guo "SINS/DVL integrated based on interacting multiple model approach by integrating feedback particle filter for ship navigation", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 122872J (13 October 2022); https://doi.org/10.1117/12.2640749

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