DOA estimation for multiple sparse sources with normalized observation vector clustering

This paper presents a new method for estimating the direction of arrival (DOA) of source signals whose number N can exceed the number of sensors M. Subspace based methods, e.g., the MUSIC algorithm, have been widely studied, however, they are only applicable when M > N. Another conventional independent component analysis based method allows M > N, however, it cannot be applied when M < N. By contrast, our new method can be applied where the sources outnumber the sensors (i.e., an underdetermined case M < N) by assuming source sparseness. Our method can cope with 2- or 3-dimensionally distributed sources with a 2- or 3-dimensional sensor array. We obtained promising experimental results for 3 × 4, 3 × 5 and 4×5 (#sensors × #speech sources) in a room (RT₆₀ = 120 ms).