DOA estimation of high-dimensional signals based on Krylov subspace and weighted l_(1)-norm  

作　　者：YANG Zeqi LIU Yiheng ZHANG Hua MA Shuai CHANG Kai LIU Ning LYU Xiaode 

机构地区：[1]Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China [2]National Key Laboratory of Microwave Imaging Technology,Beijing 100190,China [3]School of Electronic Electrical and Communication Engineering,University of Chinese Academy of Sciences,Beijing 100049,China [4]Northern Institute of Electronic Equipment,Beijing 100191,China

出　　处：《Journal of Systems Engineering and Electronics》2024年第3期532-540,F0002,共10页系统工程与电子技术（英文版）

基　　金：supported by the National Basic Research Program of China。

摘　　要：With the extensive application of large-scale array antennas,the increasing number of array elements leads to the increasing dimension of received signals,making it difficult to meet the real-time requirement of direction of arrival(DOA)estimation due to the computational complexity of algorithms.Traditional subspace algorithms require estimation of the covariance matrix,which has high computational complexity and is prone to producing spurious peaks.In order to reduce the computational complexity of DOA estimation algorithms and improve their estimation accuracy under large array elements,this paper proposes a DOA estimation method based on Krylov subspace and weighted l_(1)-norm.The method uses the multistage Wiener filter(MSWF)iteration to solve the basis of the Krylov subspace as an estimate of the signal subspace,further uses the measurement matrix to reduce the dimensionality of the signal subspace observation,constructs a weighted matrix,and combines the sparse reconstruction to establish a convex optimization function based on the residual sum of squares and weighted l_(1)-norm to solve the target DOA.Simulation results show that the proposed method has high resolution under large array conditions,effectively suppresses spurious peaks,reduces computational complexity,and has good robustness for low signal to noise ratio(SNR)environment.

关 键 词：direction of arrival(DOA) compressed sensing(CS) Krylov subspace l_(1)-norm dimensionality reduction 

分 类 号：TN820[电子电信—信息与通信工程] TN911.7