基于区间灰数的分布式多目标航迹关联算法

 研究了存在系统误差条件下分布式多目标航迹关联问题,以异地配置的2D组网雷达为背景,分析了时变系统误差对雷达上报航迹的影响,将误差影响下的目标定位看做一种认知不确定性,并给出两种用区间灰数描述这一不确定性的方法。由此提出了一种航迹关联算法,该算法以区间相离度作为衡量航迹间差异信息的测度,建立灰色关联分析模型,并根据灰关联度排序给出航迹关联对。通过对算法的约束条件进行深层次分析,给出了使用算法的先决条件。在常见系统误差环境下的蒙特卡罗仿真结果表明,算法具有良好的抗差性能和较广泛的适用性。

A Track Association Algorithm for Distributed Multi-target Systems Based on Gray Interval Numbers

This paper mainly discusses the distributed multi-target track association problems with system bias. It analyzes how the time-variable bias affects the detected tracks in a remote configured 2D radar network, regards the target location as a cognitive uncertainty, and puts forward two methods, with which the uncertainty can be expressed into gray interval numbers. Starting from this, a track association algorithm is presented. This algorithm selects the interval deviation degree as a measure of the difference between tracks, establishes a gray correlation analysis model, and points out the associated pair by a gray relational degree order. Further research is done about the constraint condition of the algorithm and the prerequisites to use the algorithm are provided. Monte Carlo simulations for common system bias show that this algorithm has good robust performance and wide practicability.