优化粒子滤波在重力辅助定位中的应用

The application of optimization particle filter in gravity aided positioning

为克服常规粒子滤波在重力辅助定位中由于观测维数增加,系统后验概率密度尖峰变窄而加剧粒子退化,难以保证算法稳定性问题.将人工物理优化过程引入粒子滤波的重采样过程,实现粒子分布优化,将算法应用于重力辅助定位,利用惯性导航系统海上试验数据进行数值实验分析,对比不同算法在相同条件下定位误差估计效果.结果表明:人工物理优化能够改善粒子退化和样本贫化问题,提高了算法的稳定性;优化后,算法可以用于重力辅助定位,并获得了较高的定位精度.

To overcome the problem that the algorithm is not stability because the observation dimensions increases in gravity gradient aided positioning,the system posterior probability density narrows and the particle degradation of the conventional particle filter intensifies,an improved particle filter based on artificial physics is introduced to optimize the particle distribution.The improved method incorporates Artificial Physics Optimization into resampling process of the generic particle filter to overcome the problem of particle degradation and sample impoverishment.The improved particle filter is applied into gravity gradient aided positioning by combining the sea experiment data of an inertial navigation system.The artificial physical optimization enables to improve particle degradation and optimize algorithm stability.After optimizing,the algorithm is adopted in gravity aided positioning,which has better estimation precision.