Abstract
We present a DOA estimation algorithm, called Joint-Sparse DOA to address the problem of Direction-of-Arrival (DOA) estimation using sensor arrays. Firstly, DOA estimation is cast as the joint-sparse recovery problem. Then, norm is approximated by an arctan function to represent joint sparsity and DOA estimation can be obtained by minimizing the approximate norm. Finally, the minimization problem is solved by a quasi-Newton method to estimate DOA. Simulation results show that our algorithm has some advantages over most existing methods: it needs a small number of snapshots to estimate DOA, while the number of sources need not be known a priori. Besides, it improves the resolution, and it can also handle the coherent sources well.
Highlights
Direction-of-Arrival (DOA) estimation using sensor arrays has been an active research area, playing a fundamental role in many applications involving electromagnetic, acoustic, and communication systems [1]
MUSIC, ESPRIT and the maximum likelihood method all rely on the statistical properties of the data, and require a Sensors 2011, 11 sufficiently large number of samples for accurate estimation
We compare the spatial spectrum of JSDOA to those of beamforming [2], MUSIC [3], and L1-SVD [13] under various snapshot scenartios
Summary
Direction-of-Arrival (DOA) estimation using sensor arrays has been an active research area, playing a fundamental role in many applications involving electromagnetic, acoustic, and communication systems [1]. JLZA-DOA is proposed in [10]; it minimizes a mixed L2,0 norm to deal with the joint-sparse recovery problem, and a fixed point method is used for DOA estimation This algorithm doesn’t satisfy numerical stability, as matrix inversion is inevitable in every iteration. The proposed algorithm has some advantages over most existing methods: it needs a small number of snapshots to estimate DOA, an the number of sources need not be known a priori. It improves the probability of resolution, and it can handle coherent sources well.
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