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Proceedings Paper

Maximum-likelihood methods for array processing based on time-frequency distributions
Author(s): Yimin Zhang; Weifeng Mu; Moeness G. Amin
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Paper Abstract

This paper proposes a novel time-frequency maximum likelihood (t-f ML) method for direction-of-arrival (DOA) estimation for non- stationary signals, and compares this method with conventional maximum likelihood DOA estimation techniques. Time-frequency distributions localize the signal power in the time-frequency domain, and as such enhance the effective SNR, leading to improved DOA estimation. The localization of signals with different t-f signatures permits the division of the time-frequency domain into smaller regions, each contains fewer signals than those incident on the array. The reduction of the number of signals within different time-frequency regions not only reduces the required number of sensors, but also decreases the computational load in multi- dimensional optimizations. Compared to the recently proposed time- frequency MUSIC (t-f MUSIC), the proposed t-f ML method can be applied in coherent environments, without the need to perform any type of preprocessing that is subject to both array geometry and array aperture.

Paper Details

Date Published: 2 November 1999
PDF: 12 pages
Proc. SPIE 3807, Advanced Signal Processing Algorithms, Architectures, and Implementations IX, (2 November 1999); doi: 10.1117/12.367666
Show Author Affiliations
Yimin Zhang, Villanova Univ. (United States)
Weifeng Mu, Villanova Univ. (United States)
Moeness G. Amin, Villanova Univ. (United States)

Published in SPIE Proceedings Vol. 3807:
Advanced Signal Processing Algorithms, Architectures, and Implementations IX
Franklin T. Luk, Editor(s)

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