Dimension decomposition algorithm for multiple source localization using uniform circular array

A dimension decomposition (DIDE) method for multiple incoherent source localization using uniform circular array (UCA) is proposed. Due to the fact that far-field signal can be considered as state where range parameter of near-field infinite, algorithm also suitable estimating direction arrival (DOA) signals. By decomposing first and second exponent term steering vector, three-dimensional (3-D) transformed into two-dimensional (2-D) one-dimensional (1-D) estimation. First, by partitioning received data, we exploit propagator acquire noise subspace. Next, objective function established partial derivative applied spatial spectrum 2-D DOA. At last, estimated DOA utilized calculate phase decomposed least squares (LS) performed parameters. In comparison existing algorithms, proposed DIDE requires neither eigendecomposition covariance matrix nor search process spectrum, which achieve satisfactory reduce computational complexity. Simulations are implemented illustrate advantages method. Moreover, simulations demonstrate classify mixed