Efficiency Evaluation of the Unconditional Maximum Likelihood Estimator for Near-Field DOA Estimation

In this paper, we address the problem of closely spaced source localization using sensor array processing. In particular, the performance efficiency (measured in terms of the root mean square error) of the unconditional maximum likelihood (UML) algorithm for estimating the direction of arrival (DOA) of near-field sources is evaluated. Four parameters are considered in this evaluation: angular separation among sources, signal-tonoise ratio (SNR), number of snapshots, and number of sources (multiple sources). Simulations are conducted to illustrate the UML performance to compute the DOA of sources in the near-field. Finally, results are also presented that compare the performance of the UML DOA estimator with the existing multiple signal classification approach. The results show the capability of the UML estimator for estimating the DOA when the angular separation is taken into account as a critical parameter. These results are consistent in both low SNR and multiplesource scenarios.

[1]  M. Viberg,et al.  Two decades of array signal processing research: the parametric approach , 1996, IEEE Signal Process. Mag..

[2]  Ilan Ziskind,et al.  Maximum likelihood localization of multiple sources by alternating projection , 1988, IEEE Trans. Acoust. Speech Signal Process..

[3]  J. Capon.  High-resolution frequency-wavenumber spectrum analysis , 1969 .

[4]  D.H. Covarrubias-Rosales,et al.  Improving resolution on DOA estimation in a multipath macrocell environment using eigenstructure-based methods , 2004, SympoTIC '04. Joint 1st Workshop on Mobile Future & Symposium on Trends In Communications (IEEE Cat. No.04EX877).

[5]  Arye Nehorai,et al.  Concentrated Cramer-Rao bound expressions , 1994, IEEE Trans. Inf. Theory.

[6]  Hakan Ali Cirpan,et al.  Deterministic Maximum likelihood Approach for Localization of Near-field Sources , 2002 .

[7]  R. O. Schmidt,et al.  Multiple emitter location and signal Parameter estimation , 1986 .

[8]  S. Shamsunder,et al.  High-order subspace-based algorithms for passive localization of near-field sources , 1995, Conference Record of The Twenty-Ninth Asilomar Conference on Signals, Systems and Computers.

[9]  Harry L. Van Trees,et al.  Optimum Array Processing , 2002 .

[10]  Don H. Johnson,et al.  Array Signal Processing: Concepts and Techniques , 1993 .

[11]  T. Moon.  The expectation-maximization algorithm , 1996, IEEE Signal Process. Mag..

[12]  H. A. Çırpan,et al.  Unconditional Maximum Likelihood Approach for Localization of Near-Field Sources: Algorithm and Performance Analysis , 2003 .

[13]  Ming-Hui Li,et al.  Improving the performance of GA-ML DOA estimator with a resampling scheme , 2004, Signal Process..