A Grid-Less Approach to Underdetermined Direction of Arrival Estimation Via Low Rank Matrix Denoising

The problem of direction of arrival (DOA) estimation of narrowband sources using an antenna array is considered where the number of sources can potentially exceed the number of sensors. In earlier works, the authors showed that using a suitable antenna geometry, such as the nested and coprime arrays, it is possible to localize O(M-2) sources using M sensors. To this end, two different approaches have been proposed. One is based on an extension of subspace based methods such as MUSIC to these sparse arrays, and the other employs l(1) norm minimization based sparse estimation techniques by assuming an underlying grid. While the former requires the knowledge of number of sources, the latter suffers from basis mismatch effects. In this letter, a new approach is proposed which overcomes both these weaknesses. The method is hybrid in nature, using a low rank matrix denoising approach followed by a MUSIC-like subspace method to estimate the DOAs. The number of sources is revealed as a by-product of the low rank denoising stage. Moreover, it does not assume any underlying grid and thereby does not suffer from basis mismatch. Numerical examples validate the effectiveness of the proposed method when compared against existing techniques.