Affine Projection Algorithm for Censored Regression

Autor:	Feng Zhao, Haiquan Zhao, Pucha Song, Lijun Zhou, Gen Wang
Rok vydání:	2021
Předmět:	Adaptive filter Censored regression model Background noise Range (mathematics) Computer science Face (geometry) Convergence (routing) System identification Electrical and Electronic Engineering Projection (set theory) Algorithm
Zdroj:	IEEE Transactions on Circuits and Systems II: Express Briefs. 68:3602-3606
ISSN:	1558-3791 1549-7747
DOI:	10.1109/tcsii.2021.3079153
Popis:	Non-Gaussian colored input signal and censored observation are encountered frequently in many practical applications of adaptive signal processing. Conventional adaptive algorithms will face convergence performance degradation in such cases. To address the issue, the affine projection algorithm for censored regression (CR-APA) is proposed in the paper. The censored thresholds and the variance of the background noise are regarded as the prior knowledge to compensate for the bias caused by censored observation. By theoretical analysis, the range of the step size is derived to guarantee the stabilization of the CR-APA. Besides, the method of evolving projection order is employed to improve the convergence performance of the CR-APA. Computer simulations in system identification and echo cancellation applications are performed to demonstrate the better convergence performance of the proposed algorithms in censored data processing over competing algorithms, and to verify the improvement of the CR-APA by the method of evolving projection order. The proposed CR-APA and CR-EAPA can be used in engineering applications to make up for the hardware.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::07daeb67ae92263ea24cd32345f79886 https://doi.org/10.1109/tcsii.2021.3079153 Zobrazit plný text záznamu