电子学报 ›› 2016, Vol. 44 ›› Issue (10): 2300-2307.DOI: 10.3969/j.issn.0372-2112.2016.10.003

• 学术论文 • 上一篇    下一篇

新生目标强度未知的单量测PHD滤波器

徐从安, 熊伟, 刘瑜, 何友   

  1. 海军航空工程学院信息融合研究所, 山东烟台 264001
  • 收稿日期:2015-03-26 修回日期:2015-11-01 出版日期:2016-10-25 发布日期:2016-10-25
  • 作者简介:徐从安,男,1987年生于山东日照.海军航空工程学院信息融合研究所博士.研究方向为随机集多目标跟踪理论、数据处理.E-mail:xcatougao@163.com;熊伟,男,1976年生于江西.海军航空工程学院信息融合研究所教授,硕士生导师.研究方向为多源信息融合、系统仿真、态势估计与评估等;刘瑜,男,1986年生于湖南邵阳.海军航空工程学院信息融合研究所讲师.研究方向为多源信息融合、分布式状态估计等;何友,男,1956年生于吉林磐石.中国工程院院士,海军航空工程学院信息融合研究所教授,博士生导师.研究方向为多源信息融合、雷达自适应检测、系统仿真与作战模拟等.

A Single Measurement PHD Filter with Unknown Target Birth Intensity

XU Cong-an, XIONG Wei, LIU Yu, HE You   

  1. Research Institute of Information Fusion, Naval Aeronautical and Astronautical University, Yantai, Shandong 264001, China
  • Received:2015-03-26 Revised:2015-11-01 Online:2016-10-25 Published:2016-10-25

摘要:

自适应新生目标强度PHD滤波器(PHD-M)在目标漏检时易发生错估或漏估,从而导致滤波器估计性能下降.为解决这一问题,提出了一种新生目标强度未知的单量测(single measurement)PHD滤波器(PHD-SM)并给出了其粒子实现方式.该文首先通过构建一步虚拟量测对漏检目标进行补偿,然后基于单量测PHD分解技术推导了PHD预测和更新公式,最后设计了一种无须聚类操作的多目标状态估计方法.仿真实验表明,在当检测概率PD较小时,PHD-SM滤波器估计性能优于PHD-M滤波器,且检测概率越小,性能优势越明显.

关键词: 多目标跟踪, 概率假设密度, 新生目标强度未知, 单量测, 一步虚拟量测

Abstract:

In situations where the targets cannot be detected in the surveillance region,the estimated performance of the adaptive target birth intensity probability hypothesis density (PHD) filter will get worse because of false or low estimate.To overcome this problem,with unknown target birth intensity,a single measurement PHD (PHD-SM) filter and its sequential Monte Carlo (SMC) method are proposed.First,the undetected targets are compensated through developing the one step virtual measurement set.Afterward,according to the single measurement decomposition technique of PHD,the predication and update equations are derived.Finally,a novel multi-target state estimation method is presented.The simulation results show that,when the detection probability PD is small,PHD-SM filter has higher estimation performance.Moreover,the smaller the detection probability,the more significant advantage of estimation performance for PHD-SM filter.

Key words: multi-target tracking, probability hypothesis density, unknown target birth intensity, single measurement, one step virtual measurement

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