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| Underwater Terrain Aided Navigation Method Based on Improved Particle Filter Algorithm |
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| DOI：10.7643/issn.1672-9242.2022.06.000 |
| KeyWord:autonomous underwater vehicle autonomous navigation low-resolution underwater maps particle filter, particle jitter correlation coefficient, navigation accuracy |
| Author | Institution |
| CHEN Rui-wei |
School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai , China |
| CHE Chi-dong |
School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai , China |
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| Abstract: |
| This paper aims to solve the problem of low navigation accuracy when the current particle filter algorithm is used for autonomous underwater vehicles (AUV) based on low-resolution underwater maps of polar regions. A particle filter method with self-jitter and correction (SJCPF) is proposed, which introduces particle jitter in the state transition process, and introduces additional process noise every time the particle position is updated, so that the over-concentrated particles in the traditional algorithm are appropriately moved to the surroundings. It improves the lack of particle diversity caused by the algorithm itself and the low resolution of the chart; in the re-sampling step, the correlation coefficient is introduced to modify the weights to further increase the particle diversity and the robustness of the algorithm. The simulation of traditional PF and SJCPF shows that, compared with traditional PF algorithm, SJCPF navigation root mean square error is reduced by 27.7%, navigation accuracy and robustness have been significantly improved. The navigation performance of SJCPF is better than traditional PF. The Pearson correlation coefficient is selected, and the larger number of particles and higher measurement frequency is chosen within an appropriate range, which can take into account the endurance and navigation accuracy of AUV. |
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