首页|DOA estimation of high-dimensional signals based on Krylov subspace and weighted l1-norm
DOA estimation of high-dimensional signals based on Krylov subspace and weighted l1-norm
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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)esti-mation due to the computational complexity of algorithms.Tradi-tional 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 esti-mation accuracy under large array elements,this paper pro-poses a DOA estimation method based on Krylov subspace and weighted l1-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 func-tion based on the residual sum of squares and weighted l1-norm to solve the target DOA.Simulation results show that the pro-posed 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.
direction of arrival(DOA)compressed sensing(CS)Krylov subspacel1-normdimensionality reduction
YANG Zeqi、LIU Yiheng、ZHANG Hua、MA Shuai、CHANG Kai、LIU Ning、LYU Xiaode
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Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China
National Key Laboratory of Microwave Imaging Technology,Beijing 100190,China
School of Electronic Electrical and Communication Engineering,University of Chinese Academy of Sciences,Beijing 100049,China
Northern Institute of Electronic Equipment,Beijing 100191,China
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