从认知雷达的角度出发,综合考虑跟踪模型和波形选择,提出一种能够适应目标运动状态急剧变化的波形自适应机动目标跟踪算法。首先,将匀速运动模型和当前统计模型作为交互式多模型(IMM)的模型集,并结合贝叶斯理论提出一种时变转移概率的自适应IMM算法。然后,结合量测误差椭圆与目标状态预测误差椭圆正交理论,研究了基于基带脉冲波形模糊函数旋转的波形库实现方法并给出了波形自适应选择跟踪算法的具体步骤。仿真实验表明,所提算法能够适应目标不同加速度机动,雷达系统跟踪性能得到了较大幅度提升。

In the viewpoint of cognitive radar, a waveform-adaption maneuvering target tracking algorithm is presented. The algorithm considers both the tracking model and waveform selection to suit the intensely varying of target motion. Firstly, constant velocity (CV) model and current statistic (CS) model are used as the model sets of interacting multiple model (IMM). A time-varying transition probability adaptive IMM algorithm is proposed according to the Bayesian theory. Then, according to the orthogonal theory of measurement error ellipse and state predicted error ellipse, the method of waveform library is given, which is based on the rotation of base-band pulse waveform ambiguity function. The step to realize the waveform-adaption tracking algorithm is also given. Simulation results show that the algorithm can satisfy different maneuver of target, and the tracking performance of proposed algorithm is improved sharply.

:TN957. 52

国家自然科学基金资助项目(61671453);安徽省自然科学基金资助项目(1608085MF123)