EIGEN VECTOR ALGORITHM FOR 3-D NEAR-FIELD MULTIPLE SOURCES LOCALIZATION

Most exiting array signal processing techniques for direction finding at present rely heavily on the far-field assumption. When the sources are located close to the array, these techniques may no longer perform satisfactorily. In this paper an eigen vector (EV) algorithm for 3-D near-field multiple sources localization is proposed. New algorithm has low computational quantities on 3-D near-field multiple sources localization by using 2-D near-field multiple sources localization algorithm twice. It also has the properties of high resolution compared with the MUSIC algorithm. Keywordsnear-field sources, eigen vector algorithm, 3-D Localization Introduction: An important problem in a wide variety of application such as radar, sonar, speech, communication, etc. is locating in space the sources of signals received by an array of sensors. Most attention has been restricted to the model of far-field. In 2-D far-field source localization, a source's location is characterized only by its bearing angle because the wavefront from a far-field source can be assumed to be planar for a uniform linear array of sensor (ULA). However, when sources are located close to the array the assumption is no longer valid. The wavefront in such cases is spherical. In 2-D near-field source localization, a source's location is characterized by its bearing angle and range. The increase in number of parameter caused the algorithm based on the far-field assumptation can no longer be directly applied so substitution must be found. Some work addressing the near-field problem in 2-D has been performed. Spatial WignerVille[1] algorithm proposed by A.L. Swindlehurst et al. can locate the near-field sources but it requires large array if high resolution is needed. Huang et al[2] proposed the 2-D MUSIC algorithm which requires 2-D grid search on the bearing angle and range to identify the peak of MUSIC spectrum. This algorithm is easily to simulate but it is computed inefficiently. Weiss et al. [3] estimated source locations using a polynomial rooting method thereby reducing the computational burden to some extent, however, the algorithm still requires bulk of computations. Starer et al. [4] and Lee et al. [5] proposed pass-following algorithms to reduce the computational burden with two 1-D searches. Although researchers have shown much interest in 3-D sources localization, the problem has not been widely treated in the literature. 3-D localization involves the estimation of spherical coordinates, namely azimuth, elevation, and range. Hung et al. [6] proposed an algorithm with exact model which is a 3-D extension of algorithm proposed by Weiss et al. But in this algorithm the pairing problem for multiple sources is not addressed. Challa et al. [7] proposed an algorithm using Unitary ESPRIT which solves the paring problem but requires a large amount of computation. Karim et al. [8] proposed a second order statistics based approach and considered two pairing techniques. Lee et al. [9] proposed a 3-D MUSIC algorithm which requires three 1-D searches with three uniform linear subarrays. But it can not solve the paring problem efficiently when the sources number is large. In this paper we presents a 2-D eigen vector algorithm with two uniform linear subarrays to locate the near-field sources in 3-D. It has low computational quantities and the paring problem is well solved. And it has the properties of high resolution, low inutile signal-noise ratio threshold compared with the MUSIC algorithm. Results: 1. 2-D near-field source localization model and assumption 2-D near field source localization geometry model is shown in figure 1