Title :
Optimal and self-tuning deconvolution in time domain
Author :
Zhang, Huanshui ; Xie, Lihua ; Soh, Yeng Chai
Author_Institution :
Sch. of Electr. & Electron. Eng., Nanyang Technol. Inst., Singapore
fDate :
8/1/1999 12:00:00 AM
Abstract :
This paper is concerned with both the optimal (minimum mean square error variance) and self-tuning deconvolution problems for discrete-time systems. When the signal model, measurement model, and noise statistics are known, a novel approach for the design of the optimal deconvolution filter, predictor, and smoother is proposed based on projection theory and innovation analysis in the time domain. The estimators are given in terms of an autoregressive moving average (ARMA) innovation model and one unilateral linear polynomial equation, where the ARMA innovation model is obtained by performing one spectral factorization. A self-tuning scheme can be incorporated when the noise statistics, the input model, and/or colored noise model are unknown. The self-tuning estimator is designed by identifying two ARMA innovation models
Keywords :
autoregressive moving average processes; circuit optimisation; deconvolution; discrete time systems; filters; mean square error methods; noise; parameter estimation; smoothing methods; statistical analysis; time-domain analysis; ARMA innovation model; autoregressive moving average; colored noise model; discrete-time systems; innovation analysis; input model; measurement model; minimum mean square error variance; noise statistics; optimal deconvolution; optimal deconvolution filter; predictor; projection theory; self-tuning deconvolution; self-tuning estimator; signal model; smoother; spectral factorization; time domain; unilateral linear polynomial equation; Autoregressive processes; Colored noise; Deconvolution; Mean square error methods; Noise measurement; Predictive models; Signal design; Statistical analysis; Technological innovation; Time measurement;
Journal_Title :
Signal Processing, IEEE Transactions on