基于改进粒子滤波算法的水下地形辅助导航方法

目的 解决当前的粒子滤波算法用于水下航行器(AUV)基于极地区域的低分辨率海图时导航精度较低的问题。方法 提出了一种带有自抖动及修正的粒子滤波方法(SJCPF)，在状态转移过程中引入粒子抖动，每次粒子位置更新时，引入额外的过程噪声，使得传统算法中过度集中的粒子适当向周围发散，改善算法本身及海图分辨率低带来的粒子多样性匮乏。在重采样步骤中，引入相关系数用于修正权值，进一步增加粒子多样性及算法的鲁棒性。结果 对传统PF及SJCPF进行仿真，相较于传统PF算法，SJCPF的导航均方根误差降低了27.7%，导航精度及鲁棒性都有显著的提升。结论 SJCPF的导航性能优于传统PF，选用皮尔逊相关系数，并在适当范围内选择较大的粒子数量和较高的测量频率，可以兼顾AUV的续航与导航精度。

Underwater Terrain Aided Navigation Method Based on Improved Particle Filter Algorithm

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.