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Adaptive_filtering_based_on_Legendre_polynomials.pdf (1.6 MB)

Adaptive Filtering Based on Legendre Polynomials

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posted on 18.10.2020 by Lu Shen, Yuriy Zakharov, Long Shi, Benjamin Henson
Abstract:

In system identification scenarios, classical adaptive filters, such as the recursive least squares (RLS) algorithm, predict the system impulse response. If a tracking delay is acceptable, interpolating estimators capable of providing more accurate estimates of time-varying impulse responses can be used; channel estimation in communications is an example of such applications. The basis expansion model (BEM) approach is known to be efficient for non-adaptive (block) channel estimation in communications. In this paper, we combine the BEM approach with the sliding-window RLS (SRLS) algorithm and propose a new family of adaptive filters. Specifically, we use the Legendre polynomials, thus the name the SRLS-L adaptive filter. The identification performance of the SRLS-L algorithm is evaluated analytically and via simulation. The analysis shows significant improvement in the estimation accuracy compared to the SRLS algorithm and a good match between the theoretical and simulation results. The performance is further investigated in application to the self-interference cancellation in full-duplex underwater acoustic communications, where a high estimation accuracy is required. A field experiment conducted in a lake shows significant improvement in the cancellation performance compared to the classical SRLS algorithm.

Funding

EPSRC EP/P017975/1

EPSRC EP/R003297/1

History

Email Address of Submitting Author

ls1215@york.ac.uk

ORCID of Submitting Author

0000-0001-8830-2238

Submitting Author's Institution

University of York

Submitting Author's Country

United Kingdom

Licence

Exports