  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 is
composed of a set of basis vectors. A low-rank auxiliary parameter vector is
then employed to compute the output power spectrum. Alternating optimization
strategies based on recursive least squares (RLS), denoted as ALRD-RLS and
modified ALRD-RLS (MARLD-RLS), are devised to compute the basis vectors and the
auxiliary parameter vector. Simulations for large sensor arrays with both
uncorrelated and correlated sources are presented, showing that the proposed
algorithms are superior to existing techniques.
