Title :
Adaptive parameter estimation of autoregressive signals from noisy observations
Author_Institution :
Sch. of Sci., Western Sydney Univ., Kingswood, NSW, Australia
Abstract :
This paper presents a new type of improved least-squares (ILS) algorithm for adaptive parameter estimation of autoregressive (AR) signals from noisy observations. Unlike the previous ILS-based methods, the developed algorithm can give consistent parameter estimates in a very direct manner that does not involve dealing with an augmented noisy AR model. The new algorithm is demonstrated to outperform the previous ILS-based methods in terms of its improved numerical efficiency
Keywords :
adaptive signal processing; autoregressive processes; least squares approximations; parameter estimation; AR signals; ILS algorithm; adaptive parameter estimation; autoregressive signals; improved least-squares algorithm; noisy observations; numerical efficiency; performance; Adaptive estimation; Australia; Equations; Multilevel systems; Noise cancellation; Parameter estimation; Signal processing algorithms; Speech analysis; White noise; Yield estimation;
Conference_Titel :
Signal Processing Proceedings, 1998. ICSP '98. 1998 Fourth International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-4325-5
DOI :
10.1109/ICOSP.1998.770247
