Root-MUSIC fast decoherence algorithm for uniform circular array eigenvector reconstruction
To address the issue that the feature vector reconstruction algorithm,which performs well on U-niform Linear Arrays(ULA),cannot be directly applied to Uniform Circular Arrays(UCA)for Direction of Arrival(DOA)estimation of coherent signals,this paper proposes a Mode and Eigenvector Reconstruc-tion MUSIC(MERM)algorithm.Utilizing Davies'mode space transformation method,the UCA is prepro-cessed and transformed into a Virtual Uniform Linear Array(VULA).By performing eigenvalue decom-position on the received data matrix and extracting the dominant eigenvector,the covariance matrix is recon-structed,enabling the steering vector of the UCA to attain a Vandermonde structure.This approach also reduces the impact of direction-finding errors after preprocessing.Finally,the azimuth angles of coherent signals are estimated using the Root-MUSIC algorithm.The MERM algorithm demonstrates strong capa-bilities in resolving coherent signals,high direction-finding accuracy,and does not require spatial smoothing,thereby reducing computational load and enhancing algorithmic real-time performance.Simula-tion experiments have validated the feasibility of this algorithm.
uniform circular arraycoherent signaleigenvectormode space transformationmatrix reconstruction
万方数据
维普
