A New Transform-Domain Regularized Recursive Least M-Estimate Algorithm for a Robust Linear Estimation

S. C. Chan, Z. G. Zhang, Y. J. Chu
2011 IEEE Transactions on Circuits and Systems - II - Express Briefs  
This brief proposes a new transform-domain (TD) regularized M-estimation (TD-R-ME) algorithm for a robust linear estimation in an impulsive noise environment and develops an efficient QR-decomposition-based algorithm for recursive implementation. By formulating the robust regularized linear estimation in transformed regression coefficients, the proposed TD-R-ME algorithm was found to offer better estimation accuracy than direct application of regularization techniques to estimate system
more » ... ents when they are correlated. Furthermore, a QR-based algorithm and an effective adaptive method for selecting regularization parameters are developed for recursive implementation of the TD-R-ME algorithm. Simulation results show that the proposed TD regularized QR recursive least M-estimate (TD-R-QRRLM) algorithm offers improved performance over its least squares counterpart in an impulsive noise environment. Moreover, a TD smoothly clipped absolute deviation R-QRRLM was found to give a better steady-state excess mean square error than other QRRLM-related methods when regression coefficients are correlated. Index Terms-QR decomposition (QRD), recursive linear estimation and filtering, regularization, smoothly clipped absolute deviation (SCAD), system identification, transformed M-estimation (ME).
doi:10.1109/tcsii.2011.2106314 fatcat:v524wrhjore4bg7p36iakio46y