基于子带矩阵CFAR的海面慢速小目标检测算法

对于K分布海杂波环境下的目标检测，基于信息几何理论的矩阵CFAR检测器是一种有效的目标检测方法。但矩阵CFAR方法计算复杂度高且当目标多普勒频率严重偏离杂波频谱中心时，其检测性能不如自适应归一化匹配滤波器(ANMF)方法，影响其实际应用。为此，该文以滤波器组对接收信号进行滤波处理，提出一种基于滤波器组子带分解最大特征值的矩阵CFAR检测方法(FD-MEMD)，通过双杂波抑制来解决目标多普勒频率偏离杂波频谱中心时矩阵CFAR方法失效的难题。最后，仿真实验验证了所提FD-MEMD具有较好的检测性能。

Target Detecting Algorithm Based on Subband Matrix for Slow Target in Sea Clutter

The matrix Constant False Alarm Rate (CFAR) detector based on the information geometry theory is an effective method for target detection in the K-distributed sea clutter environment. However, the general matrix CFAR method has a high computational complexity and its detection performance is not as good as Adaptive Normalized Matched Filter (ANMF) when the target Doppler frequency deviates from the clutter spectrum center seriously, which affects its practical application. For this reason, considered the filtered received signal by the filter bank, a Matrix CFAR Detection method based on the Filter bank subband Decomposition of Maximum Eigenvalue (FD-MEMD) is proposed. The double clutter suppression helps to solve the problem that Matrix CFAR is invalid when the target Doppler frequency is far away the central of the clutter spectrum. Finally, the simulation results show that the improved FD-MEMD has a good detection performance.