Subspace-based DOA estimation with sliding signal-vector construction for ULA

Recently, various subspace-based direction of arrival (DOA) estimation algorithms have been proposed that do not require the computationally expensive eigen-decomposition or singular value decomposition process. In particular, a recently proposed algorithm based on the cross-correlation matrix between data vectors collected by the component uniform linear arrays (ULAs) of an L-shaped array is reported as providing the best performance with the least complexity among such algorithms. Proposed is an algorithm based on the autocorrelation matrix of the data vector collected by a single ULA and methods are proposed to further improve performance with a minimal increased in complexity.