基于最小熵的机动目标雷达检测

Maneuvering Targets Radar Detection Based on Minimum Entropy

机动目标速度的不稳定会导致雷达回波产生时变的多普勒调制,即多普勒频率徙动(Doppler frequency migration,DFM)。在相参积累过程中,这将造成信号在多普勒维上散焦,积累增益降低,目标难以被检测。当前针对DFM的补偿研究大多集中于含有二阶速度分量的匀加速目标,对于含有三阶以及更高阶运动的补偿算法还需进一步完善。本文以最小熵为代价函数,利用迭代寻优的方式,提出了一种通用的高阶运动补偿算法。该算法通过仿真以及实测数据验证,被证明能够高效准确地估计出机动目标的高阶运动分量,从而有效补偿DFM引发的散焦问题,提高积累增益,完成机动目标的相参积累检测。

The changing speed of the maneuvering target causes the time-varying Doppler modulation for the radar echo,that is,the Doppler frequency migration(DFM).In the process of coherent integration,signals containing DFM will be defocused in the Doppler domain.So,the integration gain will decrease,and the target will be difficult to detect.The current research on compensation for DFM is mostly focused on uniform acceleration targets,which only contains secondorder velocity components.However,compensation algorithms containing third-order and higher-order motions need to be further improved.This paper uses the minimum entropy as the cost function and uses the iterative optimization method to propose a general high-order motion compensation algorithm.The algorithm is verified by simulation and measured data,and it is proved that it can efficiently and accurately estimate the high-order motion components of the maneuvering target,thereby effectively compensating for the defocus problem caused by DFM,increasing the integration gain,and completing the coherent integration detection for the maneuvering target.