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Blind Separation of Nonstationary Sources Based on Spatial Time-Frequency Distributions

EURASIP Journal on Advances in Signal Processing volume 2006, Article number: 064785 (2006) Cite this article

Abstract

Blind source separation (BSS) based on spatial time-frequency distributions (STFDs) provides improved performance over blind source separation methods based on second-order statistics, when dealing with signals that are localized in the time-frequency (t-f) domain. In this paper, we propose the use of STFD matrices for both whitening and recovery of the mixing matrix, which are two stages commonly required in many BSS methods, to provide robust BSS performance to noise. In addition, a simple method is proposed to select the auto- and cross-term regions of time-frequency distribution (TFD). To further improve the BSS performance, t-f grouping techniques are introduced to reduce the number of signals under consideration, and to allow the receiver array to separate more sources than the number of array sensors, provided that the sources have disjoint t-f signatures. With the use of one or more techniques proposed in this paper, improved performance of blind separation of nonstationary signals can be achieved.

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Authors and Affiliations

  1. Wireless Communications and Positioning Lab, Center for Advanced Communications, Villanova University, Villanova, PA, 19085, USA

    Yimin Zhang & Moeness G. Amin

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  1. Yimin Zhang
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  2. Moeness G. Amin
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Correspondence to Yimin Zhang.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://doi.org/creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Zhang, Y., Amin, M.G. Blind Separation of Nonstationary Sources Based on Spatial Time-Frequency Distributions. EURASIP J. Adv. Signal Process. 2006, 064785 (2006). https://doi.org/10.1155/ASP/2006/64785

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  • DOI: https://doi.org/10.1155/ASP/2006/64785

Keywords

  • Information Technology
  • Quantum Information
  • Separation Method
  • Array Sensor
  • Source Separation