基于稀疏样本选优的机载雷达动目标检测算法

Moving target detection algorithm based on sparse recovery and sample selection for airborne radar

针对存在干扰目标的非均匀样本中机载雷达动目标检测性能下降问题,基于信号稀疏恢复技术,提出一种基于稀疏样本选优的机载雷达动目标检测算法,利用训练样本和待检测距离单元的稀疏性,选择训练样本中杂波的位置和检测单元中杂波的位置相似的训练样本,去除选优后训练样本中的干扰目标,克服干扰目标对机载雷达动目标检测性能的影响,采用处理后的训练样本和待检测距离单元的数据构建杂波协方差矩阵。通过仿真实验进行改善因子、距离单元输出功率、目标信号提取的比较,说明了本文算法能够提高机载雷达动目标检测性能。

A moving target detection algorithm based on sparse recovery and sample selection for airborne radar is proposed,which overcomes interfering target in selected training samples,to solve the degradation of moving target detection probability in heterogeneous training samples.It makes full use of the sparse property of samples,and attempts to select training samples whose clutter locations are similar to that of the sample in the cell under test.Eliminate the interfering target in the selected training samples to overcome the influence on moving target detection probability.Use the processed training samples and the data of the undetected distance cell to construct the covariance matrix.Simulation experiments are carried out to compare the improvement factor,range cell output power and target signal extraction.The comparison shows that this method can improve moving target detection probability for airborne radar.