自卫式欺骗干扰与目标信号高度相似,且二者的到达角完全相同,传统的主瓣干扰抑制算法难以对其进行抑制。针对该问题,文中在极化单输入多输出 (PSIMO) 雷达系统下,提出一种基于相对拟牛顿法的盲源分离算法。该算法利用干扰和目标的极化特性差异,通过构建重叠子阵结构计算出联合自相关矩阵,并采用相对拟牛顿法估计出分离矩阵,从而将目标和干扰信号分离在不同的通道上,实现干扰抑制作用。仿真实验结果表明,该算法能够有效抑制自卫式欺骗干扰,且在低信噪比 (SNR) 和密集干扰场景下依然具有良好的干扰抑制性能,当输入SNR为-10 dB 时,输出的目标检测概率仍可以达到51. 6%,拥有较强的鲁棒性。

The self-defensive deception jamming signal is highly similar to the target signal, and the arrival angles of both are exactly the same, so it is difficult for the traditional main-lobe jamming suppression algorithm to suppress it. In this paper, a blind source separation algorithm based on the relative quasi-Newton method is proposed to solve this problem for the polarization single input multiple output (PSIMO) radar system. At first, the algorithm explores the difference in polarization characteristics between the jamming and target to create an overlapping subarray structure for calculating the joint autocorrelation matrix. Then, the separation matrix is estimated using the relative quasi-Newton method, resulting in a separation between the jamming and target signals on different channels and the suppression of interference. The simulation results indicate that the algorithm suppresses self-defensive deception jamming effectively, and has excellent interference suppression performance in low signal to noise ratio (SNR) and dense interference scenarios. Furthermore, even when the input SNR is -10 dB, the output target detection probability still reaches 51. 6%, showing strong robustness.

TN972

国家自然科学基金资助项目(61876056)