Parameters estimation from 1-bit dithered quantized data with dependent noise
SIP '07 Proceedings of the Ninth IASTED International Conference on Signal and Image Processing
Performance limit for distributed estimation systems with identical one-bit quantizers
IEEE Transactions on Signal Processing
Nonparametric one-bit quantizers for distributed estimation
IEEE Transactions on Signal Processing
Generating dithering noise for maximum likelihood estimation from quantized data
Automatica (Journal of IFAC)
Hi-index | 754.84 |
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.