多测向站多目标交叉定位时存在定位虚假点问题,随着测向站和目标数目的增加,虚假点数量急剧增加,当测向误差较大时,正确关联率很低。针对这一难题,文中提出了一种结合时差信息的定位虚假点消除新算法,在利用测向站获取的方位角信息进行交叉定位的基础上,结合到达时差信息计算测向站之间测向数据的相似度,依据相似度最大的准则进行数据关联。仿真结果表明,改进后算法的正确关联率明显提高,从而能够更有效地消除虚假定位点。

The ghost problem exists in multi-DF station multi-target crossing-location. The number of ghost increases rapidly with the number of DF station and target. It has a very low correct association probability when the DF error is comparatively large. Aimed at this problem, the improved algorithm combined with TDOA information to eliminate ghost was presented. On the basis of crossing location using azimuth angle information, the data comparability between DF stations was computed by the algorithm combined with TDOA information, and associated data by the rule of largest data comparability. The results of the simulation experiment show that this algorithm has higher correct association probability and can eliminate location ghost more effectively.