Abstract: The recovery algorithm of compressive sensing (CS) based on L1 norm constraint may lead to many false targets and deteriorate the performance of DOA estimation. To solve the above problem， a CSDOA algorithm based on weighted L1 norm was proposed. Using the orthogonality between noise subspace and signal subspace， a weighted matrix was constructed to penalize the L1 norm constrained model. By the weighted processing， the reconstructed coefficient vector with better sparsity could be achieved by using the presented algorithm. What’s more， the spurious peaks could also be effectively suppressed. Finally， more accurate DOA estimation could be obtained. Simulation results showed the efficiency of the proposed method.

Key words: DOA estimation, compressive sensing, SVD(singular value decomposition), weighted matrix, L1 norm minimization