对于动态稀疏环境下雷达干扰机的收发隔离问题,常用的遗忘因子递归最小二乘(RLS)算法对干扰耦合路径衰减系数的辨识精度不够,以至于达不到隔离需求。针对此问题,利用可变遗忘因子RLS 算法的优势,并在此基础上,增加对待估计系统参数的稀疏约束,提出了一种稀疏约束的可变遗忘因子RLS 算法。该方法充分利用了待辨识系统的先验信息,提高了系统待辨识参数的稀疏倾向性,改善了对稀疏系统的辨识精度,并且结合了可变遗忘因子,在有效提高算法跟踪性能的基础上进一步降低了稳态误差。理论分析和仿真结果表明:该方法能够有效地用于稀疏环境下的系统辨识,提高了RLS 类算法对于稀疏系统的辨识精度,进而提高了隔离性能。

For the transceiver isolation problem of radar jammer in dynamic sparse environment, the commonly used recursive least square (RLS) algorithm of forgetting factor is not accurate enough to identify the attenuation coefficient of interference coupling path, so that it can′t meet the isolation requirements. To solve this problem, a sparse constrained variable forgetting factor RLS algorithm is proposed by making use of the advantages of the variable forgetting factor RLS algorithm and adding sparse constraints on the estimated system parameters on this basis. This method makes full use of the prior information of the system to be identified, improves the sparsity tendency of the parameters to be identified, improves the identification accuracy of the sparse system, and combines the variable forgetting factor to further reduce the steady-state error on the basis of improving the tracking performance of the algorithm. Theoretical analysis and simulation results show that this method can be effectively applied to system identification in sparse environment, and improve the identification accuracy of RLS algorithms for sparse system, thus improving the isolation performance.

TN973.3