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.

An adaptive chirp beamforming method is proposed to solve the bias problem in the direction-of-arrival (DOA) estimation of wideband chirp signals that have identical time-frequency parameters yet emanate from different directions. The proposed method, based on the steered minimum variance (STMV) method, exploits the time-frequency structure of a chirp signal to improve the DOA estimation performance by effectively suppressing the wideband chirp interferences causing the bias. Simulations are performed to demonstrate the effectiveness of the proposed method.

The current letter extends narrow band (NB) local polynomial approximation (LPA) beamforming to wide band (WB) rapidly moving sources. Instead of the conventional beamformer weight in NB LPA, the proposed method adopts the steered minimum variance (STMV) method that can achieve a high resolution with short time observations. The performance of the proposed algorithm is demonstrated via computer simulations.

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.

A computationally efficient time delay and Doppler estimation algorithm is proposed for active sonar with a Linear Frequency Modulated (LFM) signal. To reduce the computational burden of the conventional estimation algorithm, an algebraic equation is used which represents the relationship between the time delay and the Doppler in the cross-ambiguity function (CAF) of the LFM signal. The algebraic equation is derived based on the Fast Maximum Likelihood (FML) algorithm. The use of this algebraic relation enables the time delay and Doppler to be estimated with two 1-D searches instead of the conventional 2-D search.

A novel closed-form algorithm is presented for estimating the 3-D location (azimuth angle, elevation angle, and range) of a single source in a uniform circular array (UCA) with a center sensor. Based on the centrosymmetry of the UCA and noncircularity of the source, the proposed algorithm decouples and estimates the 2-D direction of arrival (DOA), i.e. azimuth and elevation angles, and then estimates the range of the source. Notwithstanding a low computational complexity, the proposed algorithm provides an estimation performance close to that of the benchmark estimator 3-D MUSIC.