摘要
针对隐身目标的跟踪,本文将贪婪量测划分的方法应用于多传感器标签多伯努利(MS-LMB)滤波器中,较好地解决了低检测概率下的多雷达跟踪问题.传统的MS-LMB滤波器一般采用吉布斯采样来解决量测划分问题.当雷达网中多数雷达对隐身目标的检测概率较低而处于漏检状态时,目标的似然权值将偏小而很难被吉布斯采样获取,从而难以准确估计隐身目标的状态.贪婪量测划分机制由于单独考虑了包含漏检项的量测集,可有效解决此问题.仿真实验结果表明,在隐身目标的跟踪中,采用贪婪量测划分的MS-LMB滤波器的滤波性能明显优于采用吉布斯采样的MS-LMB滤波器的性能.
For stealthy target tracking,we apply the greedy measurement partitioning mechanism to the multi-sensor labeled multi-Bernoulli(MS-LMB)filter to solve the problem of multi-radar tracking under low detection probability.Generally,Gibbs sampling is applied to solve the measurement partitioning problem in the traditional MS-LMB filter.However,when most radars in the radar network are in the state of missing detection due to low detection probability,the likelihood weight of the stealthy target will be too small to be easily obtained by Gibbs sampling.Thus,it’s difficult to accurately estimate the state of stealthy target.The greedy measurement mechanism can solve this problem since it separately considers the measurement set containing missing detection items.The simulation results show that the filtering performance of the MS-LMB filter with greedy measurement partitioning mechanism is obviously better than that of the MS-LMB filter with Gibbs sampling when tracking stealthy targets.
作者
孙进平
代贝宁
张玉涛
SUN Jinping;DAI Beining;ZHANG Yutao(School of Electronic Information Engineering,Beihang University,Beijing,100191,China;No.8 Research Academy of CSSC,Nanjing,Jiangsu 211153,China)
出处
《北京理工大学学报》
EI
CAS
CSCD
北大核心
2022年第12期1290-1298,共9页
Transactions of Beijing Institute of Technology
基金
国家自然科学基金资助项目(62131001,62073334)。
作者简介
孙进平(1975-),男,博士,教授,E-mail:sunjinping@buaa.edu.cn;通信作者:代贝宁(1999-),男,硕士生,E-mail:beiningdai@buaa.edu.cn.
