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Diffusion recursive total least square algorithm over adaptive networks and performance analysis
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DOI：10.1016/j.sigpro.2020.107954.png)
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Abstract
En 中文
In most existing distributed estimation algorithms, many scholars adopted the assumption that the output signal of the system is noisy and the input signal is sufficiently accurate. However, in real applications, the filter input vector usually contains noise. In such situations, it has been proved that the adaptive filtering algorithm using total least squares (TLS) method has shown better performance than classical least squares (LS) method. So in this paper, we propose a diffusion recursive total least squares (DRTLS) algorithm by using the single inverse power iterations in the distributed network. Then, we analyze the mean and mean square behaviors of the proposed DRTLS algorithm. In addition, to reduce the computational complexity of the DRTLS algorithm,an improved algorithm called DCD-DRTLS is also proposed by using the dichotomous coordinate descent (DCD) iterations. At last, simulation results illustrate the superiority of the proposed algorithms and the validity of the theoretical analysis results. (c) 2020 Published by Elsevier B.V.
Keywords:
Adaptive filtering
Distributed estimation
Recursive total least squares
Dichotomous coordinate-descent
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