|本期目录/Table of Contents|

[1]夏玉洁,张兴敢*,高 健. 雷达多目标交叉轨迹跟踪算法[J].南京大学学报(自然科学),2017,53(4):723.[doi:10.13232/j.cnki.jnju.2017.04.015]
 Xia Yujie,Zhang Xinggan*,Gao Jian. Tracking algorithm of radar multi-targets with overlapping trace[J].Journal of Nanjing University(Natural Sciences),2017,53(4):723.[doi:10.13232/j.cnki.jnju.2017.04.015]
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 雷达多目标交叉轨迹跟踪算法()
     

《南京大学学报(自然科学)》[ISSN:0469-5097/CN:32-1169/N]

卷:
53
期数:
2017年第4期
页码:
723
栏目:
出版日期:
2017-08-03

文章信息/Info

Title:
 Tracking algorithm of radar multi-targets with overlapping trace
作者:
 夏玉洁张兴敢*高 健
 南京大学电子科学与工程学院通信工程系,南京,210023
Author(s):
 Xia YujieZhang Xinggan*Gao Jian
 School of Electronics Science and Engineering,Nanjing University,Nanjing,210023,China
关键词:
 粒子滤波多目标跟踪雷达跟踪交叉轨迹粒子优化
Keywords:
 particle filtermulti-targets trackingradar target trackingparticle optimization
分类号:
TN957.51
DOI:
10.13232/j.cnki.jnju.2017.04.015
文献标志码:
A
摘要:
 针对雷达跟踪系统中的多目标运动情况,以及目标轨迹交叉重叠导致的目标丢失或跟踪错误等问题,将多目标联合状态下的加权重采样思想引入到标准粒子滤波算法中.对基本粒子滤波算法进行优化,使用离散随机变量模拟目标后验概率,存在多个目标时引入联合状态概念,用关联函数把多个目标的状态变量和观测变量表示出来,把联合状态下的采样数据加入跟踪粒子的权值更新过程,使得粒子能够根据目标间的状态变化准确预测各个目标的后验分布,从而更新目标运动估计值,避免下一时刻粒子采样分布错误.联合加权重采样优化后的粒子能够准确跟踪目标运动,粒子预测和更新也不受目标交叉运动影响,克服跟踪不稳定或跟踪误差较大的问题.仿真结果表明,改进的粒子滤波算法能够达到正确跟踪多个目标的效果,并且目标轨迹交叉时仍然能够保持较高的跟踪精确度.
Abstract:
 A series problems exist when tracking multi-targets in radar systems.The target loss and tracking error which are caused by the cross-overlapping of the targets trajectory still can’t be solved properly by now.In this paper,the weighted re-sampling method under union state is introduced which is based on the standard particle filter algorithm to track targets for the purpose of reducing target loss and tracking error mentioned above.The basic particle filter algorithm is optimized to simulate the posteriori probability of the targets by using discrete random variables.When there are multiple targets,the concept of joint states is firstly introduced in the paper.The state variables and observed variables are expressed by the correlation function defined above.Then the weights of particles will be updated by the state variables which has been sampled under union state by correlation function.So that the new particles can not only predict the posterior distribution of each target accurately according to the state change between targets,but also avoid unreasonable distribution of particles in the next time.The combined weighted-resampling algorithm can correct predictive value according the target motion.The target cross-motion does not affect the prediction and updating of the particle,and overcomes the problem of unstable tracking or large error by using this algorithm.The simulation results show that the improved particle filter can achieve the goal of tracking multiple targets correctly and the high accuracy of tracking multiple targets can still be maintained when the target trajectory crosses.

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备注/Memo

备注/Memo:
 基金项目:江苏省基础研究计划(BK20151391)
收稿日期:2017-01-16
*通讯联系人,E-mail:zhxg@nju.edu.cn
更新日期/Last Update: 2017-08-02