Abstract
This paper presents a novel algorithm for the localization of mixed far-field sources (FFSs) and near-field sources (NFSs) without estimating the source number. Firstly, the algorithm decouples the direction-of-arrival (DOA) estimation from the range estimation by exploiting fourth-order spatial-temporal cumulants of the observed data. Based on the joint diagonalization structure of multiple spatial-temporal cumulant matrices, a new one-dimensional (1-D) spatial spectrum function is derived to generate the DOA estimates of both FFSs and NFSs. Then, the FFSs and NFSs are identified and the range parameters of NFSs are determined via beamforming technique. Compared with traditional mixed sources localization algorithms, the proposed algorithm avoids the performance deterioration induced by erroneous source number estimation. Furthermore, it has a higher resolution capability and improves the estimation accuracy. Computer simulations are implemented to verify the effectiveness of the proposed algorithm.
Highlights
Source localization has received considerable attention in sensor array signal processing over the past decades [1]
Several numerical simulations are conducted to validate the performance of the proposed algorithm relative to TSMUSIC [15] and oblique projection MUSIC (OPMUSIC) [16]
It is always assumed that the source number is correctly estimated for TSMUSIC and OPMUSIC
Summary
Source localization has received considerable attention in sensor array signal processing over the past decades [1]. Most of the existing algorithms concentrate on far-field sources (FFSs), whose wavefronts are plane waves. In many practical applications, the radiating sources may lie in the Fresnel region of the array [7], which is defined as [0.62(D2/λ)1⁄2, 2D2/λ] where λ is the wavelength of the sources and D symbolizes the array aperture. In this region, the spherical wavefronts are characterized by both DOA and range parameters [8]. Traditional FFS DOA estimation algorithms are no longer applicable for near-field sources (NFSs) localization. Many advanced algorithms have been proposed for NFSs localization, including the 2-D MUSIC algorithm [8], the covariance approximation (CA) method [9,10], the weighted linear prediction method [11], and the rank-reduction (RARE) type algorithms [12,13,14]
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