北斗/INS组合导航中人工鱼群粒子滤波的应用  

Application of the Beidou/INS Integrated Navigation Artificial Fish-swarm Particle Filter

作　　者：李家森 席志红[1] 赵彦青[1] 

机构地区：[1]哈尔滨工程大学信息与通信工程学院,黑龙江哈尔滨150001

出　　处：《电子科技》2013年第6期157-161,共5页Electronic Science and Technology

摘　　要：为解决粒子滤波中的粒子退化和枯竭问题,提出一种动态人工鱼群粒子滤波算法,该算法在粒子滤波重采样过程中引入人工鱼群算法的觅食和聚群行为,并依据概率密度的动态比值动态调整人工鱼的移动步长,此算法提升了粒子的多样性,克服了粒子退化及枯竭问题;推动粒子向优选区域逼近,并提高了粒子的全局搜索能力,避免粒子陷入局部最优。将改进的动态人工鱼群粒子滤波在北斗/INS紧组合的模型上进行应用,并通过仿真与人工鱼群粒子滤波及标准粒子滤波算法PF相比较。仿真结果表明,动态人工鱼群粒子滤波可显著提高估算精度,从而为在利用北斗和INS在紧组合导航时提供了新的方法。In order to solve the problems of particle degeneracy and particle impoverishment in particle filte- ring, a new particle filter which based on the dynamic artificial fish school algorithm （DAFSA-PF） is proposed in this paper. This algorithm uses the foraging behavior and cluster behavior in resample process of particle filtering to adjust the step of artificial fish based on the dynamic ratio of probability density dynamically. The new algorithm o- vercomes the problems of particle degeneracy and particle impoverishment by increasing the diversity of particles, drives the particles to the optimum area, enhances the global search ability of particles, and avoids the particles fall- ing into local optimum areas. This DAFSA-aided Particle Filtering technology is applied in the tight coupling Com- pass/INS system model. The existing artificial fish school particle filtering algorithm is compared with the conven- tional particle filtering algorithm. Simulation results show that the DAFSA-aided Particle Filtering algorithm improves the estimation accuracy significantly, thus becoming a new method for Beidou/INS integrated navigation.

关 键 词：人工鱼群 粒子滤波 组合导航 紧组合 粒子退化 粒子枯竭 

分 类 号：TN967.1[电子电信—信号与信息处理] TP18[电子电信—信息与通信工程]