CAJ | 학술논문

针对组网无源雷达多目标跟踪问题,提出一种新的变数目多目标跟踪算法,实时估计目标数目与多目标状态.算法采用多站集中式融合策略解决无源观测的不完全性问题,采用最小二乘算法构造伪位置观测解决无源观测的非线性问题.针对变数目多目标跟踪问题,利用随机集理论将多目标状态与观测构成随机有限集,通过高斯混合概率假设密度滤波递归计算多目标状态随机有限集的后验强度,实时得到目标数目及其状态的估计.算法引进最小二乘算法估计出候选目标点进行数据关联,解决了无源观测线较近时无源数据关联精度下降问题.仿真实验验证了该算法的有效性.
침대조망무원뢰체다목표근종문제,제출일충신적변수목다목표근종산법,실시고계목표수목여다목표상태.산법채용다참집중식융합책략해결무원관측적불완전성문제,채용최소이승산법구조위위치관측해결무원관측적비선성문제.침대변수목다목표근종문제,이용수궤집이론장다목표상태여관측구성수궤유한집,통과고사혼합개솔가설밀도려파체귀계산다목표상태수궤유한집적후험강도,실시득도목표수목급기상태적고계.산법인진최소이승산법고계출후선목표점진행수거관련,해결료무원관측선교근시무원수거관련정도하강문제.방진실험험증료해산법적유효성.
A new recursive algorithm is proposed for jointly estimating the time-varying number of targets and their states through passive radar measurements.Firstly,multi-sensor central fusion scheme is adopted to improve the weak observability for passive systems.Secondly,the least square method is embedded to calculate pseudo-location measurements by which the nonlinearity is solved.Thirdly,for the scenario of the time-varying target number,the new approach involves modeling the collections of targets and measurements as random finite sets (RFSs),respectively,and applying the Gaussian mixture probability hypothesis density (GMPHD) recursion to propagate the posterior intensity,which is a first-order statistic of the random finite sets by which both the time-varying number and states of multiple targets could be estimated properly.Furthermore,data association is accomplished by all potential targets located by the least square algorithm,which could avoid the decrease of association reliability when lines of sight (LOS) from different targets are close to each other.Simulation results in a scenario of tracking targets through multiple passive sensors show the advantages of the proposed algorithm.