DocumentCode :
59771
Title :
Model-Based Signal Subspace Channel Tracking for Correlated Underwater Acoustic Communication Channels
Author :
Huang, Steven He ; Jenho Tsao ; Yang, T.C. ; Sheng-Wen Cheng
Author_Institution :
Dept. of Eng. Sci. & Ocean Eng., Nat. Taiwan Univ., Taipei, Taiwan
Volume :
39
Issue :
2
fYear :
2014
fDate :
Apr-14
Firstpage :
343
Lastpage :
356
Abstract :
Many underwater acoustic channels exhibit correlated (semideterministic) multipath arrivals. Such channels are often time-varying with extensive delay spread and yet have a limited number of degrees of freedom due to cross-path (cross-tap) correlation. Traditional least square algorithms used for channel estimation do not exploit this correlation structure. In this paper, a model-based channel tracking algorithm is proposed for correlated underwater acoustic communication channels. To exploit the cross-tap correlation, the channel impulse response (CIR) is projected into a lower dimensional signal subspace consisting of a set of uncorrelated channel components. Assuming constant bases, the channel variations are then represented via an autoregressive (AR) model of these uncorrelated components. This leads to a dynamic model of reduced dimension which can be effectively processed using a Kalman filter, yielding improved channel tracking performance. By tracking only a small number of degrees of freedom in the signal subspace, significant savings in computations can be achieved compared with the conventional recursive least square (RLS) algorithm. Performance is demonstrated with at-sea data.
Keywords :
Kalman filters; autoregressive processes; least squares approximations; underwater acoustic communication; CIR; Kalman filter; autoregressive model; channel estimation; channel impulse response; correlated multipath arrivals; correlated underwater acoustic communication channels; cross-path correlation; extensive delay spread; model-based signal subspace channel tracking algorithm; traditional least square algorithms; Channel estimation; Covariance matrices; Kalman filters; Mathematical model; Noise; Prediction algorithms; Vectors; Correlated channel; Kalman filter; model-based approach; underwater acoustic communications;
fLanguage :
English
Journal_Title :
Oceanic Engineering, IEEE Journal of
Publisher :
ieee
ISSN :
0364-9059
Type :
jour
DOI :
10.1109/JOE.2013.2251808
Filename :
6568984
Link To Document :
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