Diffusion-based distributed adaptive estimation utilizing gradient-descent total least-squares
Date
2013
Authors
Arablouei, R.
Werner, S.
Dogancay, K.
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Conference paper
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Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing / sponsored by the Institute of Electrical and Electronics Engineers Signal Processing Society. ICASSP (Conference), 2013, pp.5308-5312
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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2013 (26 May 2013 - 31 May 2013 : Vancouver, Canada)
Abstract
We develop a gradient-descent distributed adaptive estimation strategy that compensates for error in both input and output data. To this end, we utilize the concepts of total least-squares estimation and gradient-descent optimization in conjunction with a recently-proposed framework for diffusion adaptation over networks. The proposed strategy does not require any prior knowledge about the noise variances and has a computational complexity comparable to the diffusion least mean square (DLMS) strategy. Simulation results demonstrate that the proposed strategy provides significantly improved estimation performance compared with the DLMS and bias-compensated DLMS (BC-DLMS) strategies when both the input and output signals are noisy.
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Copyright 2013 IEEE