Adaptive algorithms for constrained ARMA signals in the presence of noise
- 1 August 1988
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Acoustics, Speech, and Signal Processing
- Vol. 36 (8), 1282-1291
- https://doi.org/10.1109/29.1656
Abstract
A family of algorithms is developed for adaptive parameter estimation of constrained autoregressive moving-average (ARMA) signals in the presence of noise. These algorithms utilize a priori information about the signal's properties, such as its spectral type (for example, low-pass, bandpass, etc.) or a spatial-domain characteristic. Special applications include modeling of autoregressions (AR) and signals of known spectral type in the presence of noise, signal deconvolution, image deblurring and multipath parameter estimation. Selected results of simulations are included to demonstrate the performance of the algorithms.<>Keywords
This publication has 26 references indexed in Scilit:
- Adaptive spectral factorizationIEEE Transactions on Automatic Control, 1989
- Adaptive multipath delay estimationIEEE Transactions on Acoustics, Speech, and Signal Processing, 1985
- The parsimony principle for a class of model structuresIEEE Transactions on Automatic Control, 1985
- Signal Processing in Ocean TomographyPublished by Springer Nature ,1985
- ARMA spectral estimation of time series with missing observationsIEEE Transactions on Information Theory, 1984
- On the parsimony principleInternational Journal of Control, 1982
- Asymptotic gain and search direction for recursive identification algorithmsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1980
- A parameter estimation approach to time-delay estimation and signal detectionIEEE Transactions on Acoustics, Speech, and Signal Processing, 1980
- Coherent Recombination of Acoustic Multipath Signals Propagated in the Deep OceanThe Journal of the Acoustical Society of America, 1971
- Factorization of the Covariance Generating Function of a Pure Moving Average ProcessSIAM Journal on Numerical Analysis, 1969