被动定位系统中的方位数据关联

多站测向交叉定位是无源定位方法中运用较多的一种,但该方法在复杂环境下会产生虚假定位点,而虚假定位点的快速、准确排除一直是无源定位研究中的难点,国内外许多学者始终致力于该问题的研究,提出了多种解决方法,像最小距离法、最大似然算法、拉格朗日松弛算法等,但它们或数据正确相关率较低,或计算量较大,不适用于实时处理。为此,采用了一种对目标进行方位关联的新方法,即先利用一定的准则进行方位粗关联,排除掉一部分虚假定位点,减少计算量;在此基础上再进行方位细关联,找出最有可能来自目标的方位组合。与现有算法相比,该方法可快速、准确地排除虚假定位点。仿真结果表明,利用该方法可较好地对多目标进行定位和跟踪,且计算量适中。

Bearing Measurements Association in Passive Location Systems

In all methods of passive location to get information of targets,passive cross location of multiple sensors is very popular. However this method will produce a lot of false intersection points in dense environments. Eliminating these false intersection points correctly and quickly is a key technique in passive location. Many researchers have been engaged in studying this problem and presented many solution,such as the least distance method,the maximum likelihood method,the Lagrangian relaxation algorithm. But some of these methods have a low correct association rate,or a high computation burden. In this paper,a new method is adopted to associate the bearing measurements of different passive sensors. This method firstly uses a rule to eliminate some false intersection points,so the computation burden is decreased. Then,another algorithm is used to associate the bearing measurements. Compared with the classical methods,this method can eliminate false intersection points more quickly and correctly. Simulation results show that using the method proposed passive sensors can track multiple targets at the same time,and the computation burden is moderate.