均匀重采样粒子滤波器在SINS初始对准中的应用
Application of Particle Filter With Uniform Resampling on SINS Initial Alignment
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摘要: 针对标准粒子滤波算法中存在的样本贫化问题,提出一种在随机重采样中加入均匀采样的均匀重采样方法,不但可以保证标准粒子滤波算法逼近精度,同时能通过保留被抛弃粒子的分布范围增加粒子的多样性.针对捷联惯性导航系统的初始对准问题,应用这种均匀重采样的粒子滤波算法进行了仿真研究.仿真结果表明,在初始方位失准角为10°的情况下,均匀重采样粒子滤波算法的对准精度高于标准的粒子滤波算法,初始对准的稳定性也得到了有效改善.Abstract: In order to solve the problem that traditional particle filter algorithm often collapses to a single point,this paper proposed a new particle filter algorithm by adding the uniform sampling algorithm to the random resampling.The uniform resampling algorithm improved the diversity of the particles in particle filter by inheriting the distributing bound of the abandoned particles.Then,this particle filter algorithm with uniform resampling was used to research the SINS non-Gauss and non-linear alignment problem.Simulated data show that the particle filter algorithm with uniform resampling results in more accurate alignment then the standard particle filter and improves the stability of the SINS alignment algorithm.