基于信息熵准则的认知雷达机动目标跟踪算法

针对复杂战场环境下机动目标跟踪难题,提出一种认知雷达目标跟踪算法.基于人类"感知-行动"循环思想,首先把目标径向距离、径向速度和方位等量测的克拉美罗下限近似为量测误差协方差,用信息熵描述目标跟踪的不确定性,然后以最小熵为准则建立了雷达接收端数据和发射端信号处理之间联系;为避免传统交互式多模型（Interacting Multiple Model,IMM）算法由于模型转移概率设置不合理所带来的跟踪精度下降问题,受人脑三阶段记忆机制启发,将"记忆"嵌入IMM算法,通过自适应调整模型转移概率,增强了优势模型的交互主导性,弱化了不匹配模型的不良竞争.仿真实验验证了算法的有效性.

Cognitive Radar Maneuvering Target Tracking Algorithm Based on Information Entropy Criterion

A cognitive radar target tracking algorithm is proposed for the tracking problem in complex battlefield environment.Based on the theory of human "perception-action" cycle,first,the Cramer-Rao lower bound (CRLB) of target radial distance,radial velocity and azimuth is approximated to the measurement error covariance.Then,the information entropy is used to describe the uncertainty of target tracking,and the connection between data processing in radar receiver and signal processing in radar transmitter is established with the criterion of minimum entropy.Furthermore,inspired by the three stage memory mechanism of human brain,"memory" is nested in Interacting Multiple Model (IMM) algorithm to overcome the tracking precision degradation problem when the model transition probability is set improperly.Thus,the transition probability can be adaptively adjusted to enhance the dominant model and weaken the bad competition of the mismatched model.The simulation results verify the effectiveness of the proposed algorithm.