针对复杂战场环境下舰载雷达间容易出现同频干扰的问题，提出一种基于改进乌鸦搜索算法的独立分量分析方法来分离同频信号。首先，利用反向学习策略、动态感知概率、黄金正弦算子、莱维飞行改进乌鸦搜索算法，提高算法的寻优性能与收敛速度；然后，将改进乌鸦搜索算法与独立分量分析法相结合，以峭度为目标函数，使用改进乌鸦搜索算法去寻求分离同频信号的最优分离矩阵；最后，利用该矩阵对接收的混合信号进行分离。仿真结果表明，基于改进乌鸦搜索算法的独立分量分析法能较好地分离雷达同频信号，达到抗同频干扰的目的。

Aiming at the issue of co-frequency interference between shipborne radars in complex battlefield environments, an independent component analysis technique based on an improved crow search algorithm is proposed to separate co-frequency signals. First of all, the optimization performance and convergence speed of the algorithm are enhanced by utilizing the reverse learning method, dynamic perception probability, golden sine operator, and Levy flight. Then, the algorithm is integrated with the independent component analysis technique. Taking kurtosis as the objective function, the optimal separation matrix is determined by implementing the improved crow search algorithm. Finally, the matrix is applied to separate the received mixed signals. The simulation findings demonstrate that the proposed independent component analysis technique based on the improved crow search algorithm effectively separates the radar co-frequency signals and accomplishes the goal of anti-co-frequency interference.