一种基于信息熵的雷达动态自适应选择跟踪方法

A Dynamic and Adaptive Selection Radar Tracking Method Based on Information Entropy

在现代战争中,战场环境复杂多变,为适应战场探测资源动态组织,基于信息熵,该文提出了定量度量多雷达联合观测获得目标信息量多少的方法,并给出了该信息量的下界。进而,基于最小化每个时刻多雷达观测获得的目标信息熵下界,提出了一种自适应选取信息含量高的雷达进行目标跟踪的方法。仿真结果表明,相比不采用信息熵的跟踪方法,提出的方法具有更高的跟踪精度。

Nowadays, the battlefield environment has become much more complex and variable. This paper presents a quantitative method and lower bound for the amount of target information acquired from multiple radar observations to adaptively and dynamically organize the detection of battlefield resources based on the principle of information entropy. Furthermore, for minimizing the given information entropy＇s lower bound for target measurement at every moment, a method to dynamically and adaptively select radars with a high amount of information for target tracking is proposed. The simulation results indicate that the proposed method has higher tracking accuracy than that of tracking without adaptive radar selection based on entropy.