DocumentCode :
1814323
Title :
An AI based frequency weighted least-squares filter
Author :
Mallory, G. ; Doraiswami, R.
Author_Institution :
Dept. of Electr. Eng., New Brunswick Univ., Fredericton, NB, Canada
fYear :
1995
fDate :
28-29 Sep 1995
Firstpage :
438
Lastpage :
443
Abstract :
An artificial intelligence (AI) based robust algorithm to extract, a posteriori, the rational signal model from a noisy measurement, with little a priori information, is proposed. The spectrum and the statistics of the signal and of the corrupting noise are assumed unknown except that the signal is assumed to have a rational spectrum. An algorithm based on both system and signal theory, and on heuristics is derived to select a set of frequencies where the SNR is high from a given measurement spectrum. A relative weighting which indicates the importance of the measurement at each frequency is also obtained. An estimate of the signal model is obtained from the best weighted least squares fit to the measurement spectrum at the selected frequencies. The proposed scheme has applications to control and signal processing, and is evaluated with a number of simulated examples, and on a physical system. The results are compared with conventional adaptive filter techniques
Keywords :
artificial intelligence; artificial intelligence; frequency weighted least-squares filter; heuristics; least squares fit; noisy measurement; rational signal model; rational spectrum; signal extraction; signal model; signal processing; singular value decomposition; Artificial intelligence; Data mining; Filters; Frequency estimation; Frequency measurement; Least squares approximation; Noise robustness; Signal processing algorithms; Signal to noise ratio; Statistics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Applications, 1995., Proceedings of the 4th IEEE Conference on
Conference_Location :
Albany, NY
Print_ISBN :
0-7803-2550-8
Type :
conf
DOI :
10.1109/CCA.1995.555743
Filename :
555743
Link To Document :
بازگشت