区间量测下基于无迹变换的伯努利粒子滤波算法  

Bernoulli particle filter algorithm based on unscented transformation in interval measurement

作　　者：吴孙勇[1,2] 张馨方[1] 桂丛楠 蔡如华[1] 孙希延 WU Sun-yong ZHANG Xin-fang GUI Cong-nan CAI Ru-hua SUN Xi-yan(School of Mathematics and Computational Science, Guilin University of Electronic Technology, Guilin 541004, China Guangxi Key Laboratory of Precision Navigation Technology and Application, Guilin 541004, China The 54th Research Institute ofCETC, Shijiazhuang 050081, China)

机构地区：[1]桂林电子科技大学数学与计算科学学院,广西桂林541004 [2]广西精密导航技术与应用重点实验室,广西桂林541004 [3]中国电子科技集团公司第五十四研究所,石家庄050081

出　　处：《控制与决策》2017年第8期1523-1527,共5页Control and Decision

基　　金：国家自然科学基金项目(61261033;61561016;61362005);广西自然科学基金项目(2014GXNSFAA118352;2014GXNSFBA118280;2016GXNSFAA380073);广西精密导航技术与应用重点实验室基金项目(DH201502);广西高校数据分析与计算重点实验室开放基金项目;广西密码学与信息安全重点实验室研究课题(GCIS201611)

摘　　要：针对区间量测下目标的实时检测与跟踪问题,提出基于无迹变换的伯努利粒子滤波算法(BernoulliUpf).该算法在伯努利粒子滤波算法(Bernoulli-pf)的基础上融合无迹卡尔曼滤波(UKF),融合后的算法在预测步骤产生持续存活粒子时,充分考虑到当前时刻的量测,从而引导粒子向高似然区域移动,使得粒子分布更加接近真实状态的后验分布.仿真实验表明,Bernoulli-Upf算法的估计精度优于Bernoulli-pf算法.An improved Bernoulli particle filter algorithm based on unscented transformation is proposed for target detection and tracking in the interval measurement. Under the theory framework of the particle filter, an algorithm which combines the particle filter with the unscented Kalman filter（UKF） is presented. When persistent particles are calculated during the predicted measure by using the algorithm, the persistent particles are most likely to be in the region of high likelihood based on the current measurement, which makes the particles distribution more approach to the true posterior distribution of the state. Simulation results show that the tracking error of the improved Bernoulli particle filter is less than the original algorithm.

关 键 词：目标跟踪 区间量测 伯努利滤波 无迹卡尔曼滤波 粒子滤波 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]