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 the estimation of spatio-temporal events with cheap, simple sensors, we consider the problem of estimation of a parameter thetas of a signal s(x;thetas) corrupted by noise assuming that only 1-bit precision dithered quantized samples are available. An estimate that does not require the knowledge of the dither signal and the noise distribution is proposed, and it is analyzed in detail under variety of nonidealities. The consistency and asymptotic normality of the estimate is established for deterministic and random sampling, imprecise knowledge of sampling locations, Gaussian and non-Gaussian noise (with possibly infinite variance), a wide class of dither distributions, and under erroneous transmission of the binary observations via binary-symmetric channels (BSCs). It is also shown that if approximation to the log-likelihood equation in the full precision case yields a good estimate, then there is a corresponding good estimate based on 1-bit dithered samples. The proposed estimate requires no more computation than the maximum-likelihood estimate for the full precision case and suffers only a logarithmic rate loss compared to the full precision case when uniform dithering is used. It is shown that uniform dithering leads to the best rate among a broad class of dither distributions. A condition under which no dithering leads to a better estimate is also given