一种基于无偏协方差估计RLS的自适应阵列天线抗干扰算法

Adaptive Array Antenna Anti-Jamming Algorithm Based on Unbiased Covariance Estimation RLS

递推最小二乘法(RLS)是自适应阵列天线抗干扰的主要算法之一。为提高RLS算法对遗忘因子选择健壮性,避免因遗忘因子选择不当所造成的算法不收敛问题,针对自适应阵列天线的多路接收信号,基于其无偏协方差矩阵模型,推导设计出了一种新的RLS算法,相比于常规RLS,在该算法中遗忘因子可以更加精确地控制RLS迭代过程项,降低因遗忘因子设置不当而造成的算法不收敛风险。通过仿真验证了算法的有效性。

Recursive Least Square(RLS)is one of the main anti-jamming algorithms for adaptive array antennas.In order to improve the robustness of RLS and avoid the non-convergence caused by the improper forgetting factor,using the multi-channel received signal of adaptive array antenna,a new RLS algorithm based on its unbiased covariance matrix is proposed.Compared with the conventional RLS,the forgetting factor in this algorithm can accurately control the RLS iteration item.Consequently,the risk of algorithm non-convergence caused by the improper forgetting factor is reduced.The effectiveness is verified by the simulation.