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A new noise-compensated estimation scheme for multichannel autoregressive signals from noisy observations

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Abstract

In many engineering applications concerning the recovery of signals from noisy observations, a common approach consists in adopting autoregressive (AR) models. This paper is concerned with not only the estimation of multichannel autoregressive (MAR) model parameters but also the recovery of signals. A new noise compensated parameter estimation scheme is introduced in this paper. It contains an advanced least square vector (ALSV) algorithm which not only keeps the advantage of blindly estimating the MAR parameters and the variance-covariance matrix of observation noises, but also aims at ensuring the variance-covariance matrix to be symmetric in each iterative procedure. Moreover, the estimation of variance-covariance matrix of input noise is proposed, and then we form an optimal filtering to recover the signals. In the numerical simulations, the estimation performance of the ALSV estimation algorithm significantly outperforms that of other existed methods. Moreover, the optimal filtering based on the ALSV algorithm leads to more accurate recovery of the true signals.

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Authors and Affiliations

  1. College of Computer Science and Technology, Southwest University for Nationalities, Chengdu, Sichuan, 610041, People’s Republic of China

    Xiaomei Qu

  2. College of Mathematics, Sichuan University, Chengdu, Sichuan, 610064, People’s Republic of China

    Jie Zhou & Yingting Luo

Authors
  1. Xiaomei Qu
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  2. Jie Zhou
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  3. Yingting Luo
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Correspondence to Jie Zhou.

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Qu, X., Zhou, J. & Luo, Y. A new noise-compensated estimation scheme for multichannel autoregressive signals from noisy observations. J Supercomput 58, 34–49 (2011). https://doi.org/10.1007/s11227-010-0530-z

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  • DOI: https://doi.org/10.1007/s11227-010-0530-z

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