Estimation of noisy quantized random observation coefficient AR time-series

TitleEstimation of noisy quantized random observation coefficient AR time-series
Publication TypeConference Paper
Year of Publication1994
AuthorsKrishnamurthy, V., and I. M. Y. Mareels
Conference NameInformation Theory, 1994. Proceedings., 1994 IEEE International Symposium on
Pagination115
Date Publishedjun.
Keywords1-bit quantized observations, AR signal, AR time-series, asymptotically normal estimation algorithm, auto-regressive processes, autoregressive processes, Gaussian noise, input signal, multiplicative white Gaussian noise, noisy quantized random observation coefficient, observation sequence, parameter estimation, quantisation (signal), sequential estimation, time series, white noise, Yule-Walker type system
Abstract

We present a consistent, asymptotically normal estimation algorithm for the parameters of auto-regressive (AR) processes from 1-bit quantized observations. The input signal to the quantifier is the AR signal corrupted by multiplicative white Gaussian noise. Our algorithm is computationally inexpensive as it involves counting the number of occurrences of particular patterns of zeros and ones in the observation sequence and then solving a Yule-Walker type system

URLhttp://dx.doi.org/10.1109/ISIT.1994.394873
DOI10.1109/ISIT.1994.394873

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