The problem of estimating the nominal angles and angular spreads of multiple coherently distributed (CD) sources in a symmetric uniform linear array (ULA) is considered. Based on structure of the subarrays consisting of two opposite sensors relative to the center of a ULA and the rank reduction (RARE) concept, the proposed algorithm is able to estimate the nominal angles without any angular signal density model assumptions of the sources. Using the estimated nominal angles, the angular spread of each source is then obtained using a one-dimensional (1-D) distributed source parameter estimator (DSPE).

A novel algorithm is presented for estimating the 3-D location (azimuth angle, elevation angle, and range) of multiple sources with a uniform circular array (UCA). Based on its centrosymmetric property, a UCA is divided into two subarrays. The steering vectors for these subarrays then yield a 2-D direction of arrival (DOA)-related rotational invariance property in the signal subspace, which enables 2-D DOA estimations using a generalized-ESPRIT algorithm. Based on the estimated 2-D DOAs, a range estimation can then be obtained for each source by defining the 1-D MUSIC spectrum. Despite its low computational complexity, the proposed algorithm can almost match the performance of the benchmark estimator 3-D MUSIC.

A novel algorithm is presented for near-field source localization with a symmetric uniform linear array (ULA) consisting of an even number of sensors. Based on element reordering of a symmetric ULA, the steering vector is factorised with respect to the range-independent bearing parameters and range-relevant 2-D location parameters, which allows the range-independent bearing estimation with rank-reduction idea. With the estimated bearing, the range estimation for each source is then obtained by defining the 1-D MUSIC spectrum. Simulation results are presented to validate the performance of the proposed algorithm.