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In this work, we propose an alternating low-rank decomposition (ALRD)approach and novel subspace algorithms for direction-of-arrival (DOA)estimation. In the ALRD scheme, the decomposition matrix for rank reduction iscomposed of a set of basis vectors. A low-rank auxiliary parameter vector isthen employed to compute the output power spectrum. Alternating optimizationstrategies based on recursive least squares (RLS), denoted as ALRD-RLS andmodified ALRD-RLS (MARLD-RLS), are devised to compute the basis vectors and theauxiliary parameter vector. Simulations for large sensor arrays with bothuncorrelated and correlated sources are presented, showing that the proposedalgorithms are superior to existing techniques.

Efficient Reduced-Rank DOA Estimation Algorithms Using Alternating Low-Rank Decompositions