Paper Info
Title
A class of frequency-domain adaptive approaches to blind multichannel identification
Abstract
We extend our previous studies on adaptive blind channel identification from the time domain into the frequency domain. A class of frequency-domain adaptive approaches, including the multichannel frequency-domain LMS (MCFLMS) and constrained/unconstrained normalized multichannel frequency-domain LMS (NMCFLMS) algorithms, are proposed. By utilizing the fast Fourier transform (FFT) and overlap-save techniques, the convolution and correlation operations that are computationally intensive when performed by the time-domain multichannel LMS (MCLMS) or multichannel Newton (MCN) methods are efficiently implemented in the frequency domain, and the MCFLMS is rigorously derived. In order to achieve independent and uniform convergence for each filter coefficient and, therefore, accelerate the overall convergence, the coefficient updates are properly normalized at each iteration, and the NMCFLMS algorithms are developed. Simulations show that the frequency-domain adaptive approaches perform as well as or better than their time-domain counterparts and the cross-relation (CR) batch method in most practical cases. It is remarkable that for a three-channel acoustic system with long impulse responses (256 taps in each channel) excited by a male speech signal, only the proposed NMCFLMS algorithm succeeds in determining a reasonably accurate channel estimate, which is good enough for applications such as time delay estimation.
Year
DOI
Venue
2003
10.1109/TSP.2002.806559
IEEE Transactions on Signal Processing
Keywords
Field
DocType
proposed nmcflms algorithm,time-domain multichannel,frequency-domain adaptive approach,blind multichannel identification,accurate channel estimate,frequency domain,multichannel frequency-domain,adaptive blind channel identification,unconstrained normalized multichannel frequency-domain,nmcflms algorithm,multichannel newton,fast fourier transform,adaptive filter,fast fourier transforms,frequency domain analysis,uniform convergence,adaptive signal processing,newton method,least mean square,impulse response,indexing terms,time domain,convolution
Time domain,Frequency domain,Signal processing,Mathematical optimization,Iterative method,Convolution,Fast Fourier transform,Adaptive filter,System identification,Mathematics
Journal
Volume
Issue
ISSN
51
1
1053-587X
Citations 
PageRank 
References 
105
6.39
11
Authors
2
Search Limit
100105
Name
Order
Citations
PageRank
Yiteng Huang1123998.26
Jacob Benesty21941146.01