caa a22 4500 445851546 CHVBK 20180317145415.0 cr unu---uuuuu 170323e20111001xx s 000 0 eng 10.1007/s11227-010-0530-z doi (NATIONALLICENCE)springer-10.1007/s11227-010-0530-z A new noise-compensated estimation scheme formultichannel autoregressive signals fromnoisyobservations [Elektronische Daten] [Xiaomei Qu, Jie Zhou, Yingting Luo] 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. Springer Science+Business Media, LLC, 2010 Multichannel autoregressive signals nationallicence Parameter estimation nationallicence Symmetric property nationallicence Variance-covariance matrix nationallicence Qu Xiaomei College of Computer Science and Technology, Southwest University for Nationalities, 610041, Chengdu, Sichuan, People's Republic of China aut Zhou Jie College of Mathematics, Sichuan University, 610064, Chengdu, Sichuan, People's Republic of China aut Luo Yingting College of Mathematics, Sichuan University, 610064, Chengdu, Sichuan, People's Republic of China aut The Journal of Supercomputing Springer US; http://www.springer-ny.com 58/1(2011-10-01), 34-49 0920-8542 58:1<34 2011 58 11227 https://doi.org/10.1007/s11227-010-0530-z text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11227-010-0530-z text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Qu Xiaomei College of Computer Science and Technology, Southwest University for Nationalities, 610041, Chengdu, Sichuan, People's Republic of China aut NATIONALLICENCE 700 1- Zhou Jie College of Mathematics, Sichuan University, 610064, Chengdu, Sichuan, People's Republic of China aut NATIONALLICENCE 700 1- Luo Yingting College of Mathematics, Sichuan University, 610064, Chengdu, Sichuan, People's Republic of China aut NATIONALLICENCE 773 0- The Journal of Supercomputing Springer US; http://www.springer-ny.com 58/1(2011-10-01), 34-49 0920-8542 58:1<34 2011 58 11227 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer