Blind Separation of Nonstationary Sources Based on Spatial Time-Frequency Distributions
- PDF / 1,621,156 Bytes
- 13 Pages / 600.03 x 792 pts Page_size
- 88 Downloads / 152 Views
Blind Separation of Nonstationary Sources Based on Spatial Time-Frequency Distributions Yimin Zhang and Moeness G. Amin Wireless Communications and Positioning Lab, Center for Advanced Communications, Villanova University, Villanova, PA 19085, USA Received 1 January 2006; Revised 24 July 2006; Accepted 13 August 2006 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. Copyright © 2006 Y. Zhang and M. G. Amin. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
1.
INTRODUCTION
Several methods have been proposed to blindly separate independent narrowband sources [1–8]. When the spatial (mixing) signatures of the sources are not orthogonal, blind source separation (BSS) methods usually employ at least two different sets of matrices that span the same signal subspace. One set is used for whitening purpose, whereas the other set is used to estimate rotation ambiguity so that the spatial signatures and the source waveforms impinging on a multiantenna receiver can be recovered. Different methods have been developed for blind source separation based on cyclostationarity, spectral or/and higher-order statistics of the source signals, linear and quadrature time-frequency (t-f) transforms. In this paper, we focus on the blind separation of nonstationary sources that are highly localized in the t-f domain (e.g., frequency modulated (FM) waveforms). Such signals are frequently encountered in radar, sonar, and acoustic applications [9–11]. For this kind of nonstationary signals, quadrature time-frequency distributions (TFDs) have been employed for array processing and have been found successful in blind source separations [12–16]. Among the existing methods, typically, the spatial time-frequency distribution (STFD) matrices are used for source diagonalization and
antidiagonalization, whereas the whitening matrix remains the signal covariance matrix. The STFD matrices are constructed from the auto-TFDs and cross-TFDs o
Data Loading...
