基于量测驱动新生目标强度估计的PHD滤波算法  

A Measure-Driven PHD Filtering Algorithm for Target Birth Intensity Estimation

作　　者：张祺琛 丁勇[1,2] 柏茂羽 ZHANG Qi-chen DING Yong BAI Mao-yu(Nanjing University of Aeronautics and Astronautics, College of Automation Engineering Jiangsu Key Laboratory of Internet of Things and Control Technologies, Nanjing 211106, China)

机构地区：[1]南京航空航天大学自动化学院,南京211106 [2]南京航空航天大学江苏省物联网与控制技术重点实验室,南京211106

出　　处：《电光与控制》2017年第2期13-18,24,共7页Electronics Optics & Control

基　　金：总参通指重点基金(TZLDLYYB2014002)

摘　　要：针对多目标跟踪中存在的新生目标强度未知的问题,提出一种基于量测驱动新生目标强度估计的PHD(MDTBI-PHD)滤波算法。该算法采用增广状态空间方法,在由真实目标状态与虚拟目标(杂波)状态构成的增广状态空间上实现PHD多目标跟踪。算法通过构造新生目标量测集,采用量测驱动的方式对新生目标强度进行估计,从而避免了对新生目标强度先验知识的依赖,同时,该算法也避免了未知杂波对真实目标强度估计的干扰。仿真结果表明,该算法在新生目标强度未知的情况下,具有对目标数目变化敏感的优势,可降低计算复杂度,明显提高跟踪精度。In order to solve the problem that the Target Birth Intensity （TBI） is unknown in multi-target tracking, a measure-driven PHD algorithm is proposed for TBI estimation. By using augmented state space method, PHD multi-target tracking is achieved in the augmented state space composed of the real-target state space and the spurious-target state space （clutter space）. By constructing the newborn target measure set, the TBI is estimated with the measure-driven method, which avoids both the dependence on the prior knowledge of TBI and the interference of unknown clutter to the real-target intensity estimation. Simulation results show that the proposed algorithm is sensitive to the change of target number and can reduce the computational complexity, which improves the accuracy of tracking obviously.

关 键 词：多目标跟踪 概率假设密度 新生目标强度 量测驱动 增广空间 

分 类 号：V249[航空宇航科学与技术—飞行器设计]