基于改进空间划分的目标分群算法

针对战场目标分群中存在的类数未知和阈值选取欠缺有效方法的问题,提出一种基于改进空间划分的目标分群算法。首先,通过敌我及作战单位属性划分,约减分群目标数规模,降低计算量;其次,通过对空间距离划分进行改进,能够动态地优选阈值,有效解决类数未知的分群问题。通过引入划分独立性和逆χ²分布概率区间约束,消除计算冗余并提取出候选阈值,在此基础上选取最大的候选阈值作为最终分群阈值,可以有效滤除过程噪声与观测噪声干扰,提高分群准确率。仿真结果表明,该算法对战场环境下的多目标编队分群具有良好的有效性、稳健性和实时性。

Improved space partition based target clustering algorithm

Battlefield target clustering is confronted with the problems of the unknown category number and the lack of effective threshold selection methods. To deal with these problems, an improved space partition based target clustering algorithm is proposed. Firstly, by using the friend-or-foe partition and the combat-unit attribute partition, the clustering target number is reduced to lessen the computational burden. Secondly, through improving the space partition, the optimal selection of the dynamic threshold is implemented to solve the clustering problem with the unknown category number. In particular, it can eliminate the computational redundancy and extract the candidate threshold by introducing the partition independence and the probability interval constraint of inverse cumulative χ² distribution. On this basis, selecting the maximum candidate threshold can effectively filter out the process noise and the observation noise, improving the accuracy of clustering. Simulation results show that the proposed algorithm is effective, stable and real-time under the battlefield environment.