ESTIMATION AND BLIND DECONVOLUTION OF AUTOREGRESSIVE SYSTEMS WITH NONSTATIONARY BINARY INPUTS

摘要

Abstract.The problem of parameter estimation and blind deconvolution of auto‐regressive (AR) systems with independent nonstationary binary inputs is considered. The estimation procedure consists of applying a moving‐average filter (equalizer) to the observed data and adjusting the parameters of the filter so as to minimize a criterion that measures the binariness of its output. The output sequence itself serves as an estimate of the unobservable binary input of the AR system. Without assuming stationarity of the inputs, it is shown that the proposed method produces a consistent estimator of the AR system not only in the sense of converging to the true parameter as the sample size increases, but also in the sense of attaining the true parameter of the AR system for a sufficiently large sample size. For noisy data, the estimation criterion is modified on the basis of an asymptotic analysis of the effect of the noise. It is shown that the modified criterion is also consistent (in the usual sense) and its variability depends upon the filtered noise. Some simulation results are presented to demonstrate the performance of the proposed method for parameter estimation as well as for blind deconvolut