Estimation of noisy quantized Gaussian AR time-series with randomly varying observation coefficient

Author

Krishnamurthy, Vikram ; Mareels, Iven

Author_Institution

Dept. of Electr. & Electron. Eng., Melbourne Univ., Parkville, Vic., Australia

Volume

43

Issue

5

fYear

1995

fDate

5/1/1995 12:00:00 AM

Firstpage

1285

Lastpage

1290

Abstract

Presents an estimation algorithm for the parameters of Gaussian auto-regressive AR processes from one-bit quantized observation sequences. The input signal to the quantizer is the AR signal corrupted by multiplicative white Gaussian noise. The estimation algorithm is computationally inexpensive as it involves counting the number of occurrences of particular patterns of zeros and ones in the observation sequence

Keywords

Gaussian noise; autoregressive processes; computational complexity; quantisation (signal); random processes; time series; white noise; Gaussian auto-regressive processes; estimation algorithm; multiplicative white Gaussian noise; noisy quantized Gaussian AR time-series; one-bit quantized observation sequences; ones; randomly varying observation coefficient; zeros; Australia; Gaussian noise; Linear systems; Maximum likelihood estimation; Nonlinear equations; Signal processing; Signal processing algorithms; Systems engineering and theory; Yttrium;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.382419

Filename

382419