Multivariate signal parameter estimation under dependent noise from 1-Bit dithered quantized data

被引：44

作者：

Dabeer, Onkar ^{[1
]}

Masry, Elias ^{[2
]}

机构：

[1] Tata Inst Fundamental Res, Sch Technol & Comp Sci, Bombay 400005, Maharashtra, India

[2] Univ Calif San Diego, Dept Elect & Comp Engn, La Jolla, CA 92093 USA

来源：

IEEE TRANSACTIONS ON INFORMATION THEORY | 2008年 / 54卷 / 04期

关键词：

1-bit dithered quantization; dependent data; sensor networks; signal parameters estimation;

D O I：

10.1109/TIT.2008.917637

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Motivated by applications in sensor networks and communications, we consider multivariate signal parameter estimation when only dithered 1-bit quantized samples are available. The observation noise is taken to be a stationary, strongly mixing process, which covers a wide range of processes including autoregressive moving average (ARMA) models. The noise is allowed to be Gaussian or to have a heavy-tail (with possibly infinite variance). An estimate of the signal parameters is proposed and is shown to be weakly consistent. Joint asymptotic normality of the parameters estimate is also established and the asymptotic mean and covariance matrices are identified.

引用

页码：1637 / 1654

页数：18