Passive location algorithm of two sensors based on multihypothesis extended Kalman filter

Xiu Jianjuan, He You, Xiu Jianhua, Wang Guohong
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引用次数: 1

Abstract

Multitarget tracking with bearings only measurements of two passive sensors is a very important problem, which has not been solved. To counter this problem a method is proposed in this paper, This method firstly used the bearing measurements of two passive sensors to estimate the initial range interval of targets, which are divided into several subintervals. At each subinterval an extended Kalman filter and a multihypothesis method are used to estimate the state of targets. At the same time the bearing measurements are associated. Combined state estimate is obtained as weighted sums of the state estimate of each subinterval. Simulation results show that through using the algorithm discussed in this paper two passive sensors can locate and track multiple targets at the same time.
基于多假设扩展卡尔曼滤波的双传感器无源定位算法
仅测量两个无源传感器的方位的多目标跟踪是一个非常重要的问题,一直没有得到解决。针对这一问题,本文提出了一种方法,该方法首先利用两个无源传感器的方位测量值来估计目标的初始距离区间,并将目标的初始距离区间划分为若干个子区间;在每个子区间上使用扩展卡尔曼滤波和多假设方法来估计目标的状态。同时,轴承测量是相关联的。组合状态估计是每个子区间的状态估计的加权和。仿真结果表明,采用本文所讨论的算法,两个无源传感器可以同时对多个目标进行定位和跟踪。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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