High Resolution Direction-of-Arrive Estimation Based on Sparse Reconstruction and Compressive Sensing Beamforming

A novel compression perception model is established by making use of the spatial sparsity. A random com-pression matrix is constructed by designing a new compressive sampling way with compressive sensing(CS) theory. And another compression matrix is obtained by applying approximate QR decomposition to Gaussian random matrix in order to get a better restricted isometry property(RIP) constant. Singular value decomposition(SVD) is adopted on the data matrix to extract signal subspace for getting low dimensional form of receiving data matrix. Two different kinds of methods for DOA estimation are proposed based on the new compression matrices. One is for CS recovery, i.e. QR sin-gular value decomposition multi-vectors FOCal undetermined system solve(QR-SVD-MFOCUSS); the other is for CS beamforming, i.e. random singular value decomposition compressive sensing beamforming(RSVD-CSB) and QR singu-lar value decomposition compressive sensing beamforming(QRSVD-CSB). Simulation results show that, compared to the multi-vectors FOCal undetermined system solver(MFOCUSS) algorithms, QR-SVD-MFOCUSS is suitable for low signal-to-noise ratio(SNR) condition with significant reduction of computational burden; and compared to the minimum variance distortionless response(MVDR) algorithms and the CS beamforming algorithms, the proposed method pos-sesses higher angular resolution, lower root mean square error(RMSE), better estimation performance, and so on.