Estimation of parameters of non-Gaussian non-zero mean autoregressive processes with application to optimal detection in colored noise
Document Type
Conference Proceeding
Date of Original Version
12-1-1988
Abstract
Summary form only given, as follows. The problem addressed in this study is that of estimating signal and noise parameters from a mixture of non-Gaussian autoregressive (AR) noise with partially known deterministic signal. Two models are considered in order to examine different kinds of additive mixing. The Cramer-Rao bounds to the joint estimation of the signal and the noise parameters are presented. A computationally efficient estimator, which was previously proposed for estimation in the absence of signal, is extended for the two models under consideration. The proposed method essentially consists of two stages of least-squares (LS) estimation which is motivated by the maximum-likelihood estimation (MLE). The technique is then applied to the problem of detecting a signal known except for amplitude in colored non-Gaussian noise. Two slightly different mixing models are used, and a generalized-likelihood-ratio test (GLRT), coupled with the proposed estimation scheme, is used to solve the problems. The results of computer simulations are presented as an evidence of the validity of the theoretical predictions of performance.
Volume
25 n 13
Citation/Publisher Attribution
Sengupta, Debasis, and Steven M. Kay. "Estimation of parameters of non-Gaussian non-zero mean autoregressive processes with application to optimal detection in colored noise." 25 n 13, (1988). https://digitalcommons.uri.edu/ele_facpubs/1811