Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Effects of sampling and quantization on single-tone frequencyestimation
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Marginal Likelihood for Estimation and Detection Theory
IEEE Transactions on Signal Processing
A theory of nonsubtractive dither
IEEE Transactions on Signal Processing
Quantization for Maximin ARE in Distributed Estimation
IEEE Transactions on Signal Processing - Part II
Bandwidth-constrained distributed estimation for wireless sensor Networks-part I: Gaussian case
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
Sequential signal encoding from noisy measurements using quantizers with dynamic bias control
IEEE Transactions on Information Theory
Universal decentralized estimation in a bandwidth constrained sensor network
IEEE Transactions on Information Theory
Signal Parameter Estimation Using 1-Bit Dithered Quantization
IEEE Transactions on Information Theory
Multivariate Signal Parameter Estimation Under Dependent Noise From 1-Bit Dithered Quantized Data
IEEE Transactions on Information Theory
IEEE Journal on Selected Areas in Communications
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In this paper, we consider the nonparametric distributed parameter estimation problem using one-bit quantized data from peripheral sensors. Assuming that the sensor observations are bounded, nonparametric distributed estimators are proposed based on the knowledge of the first N moments of sensor noises. These estimators are shown to be either unbiased or asymptotically unbiased with bounded and known estimation variance. Further, the uniformly optimal quantizer based only on the first moment information and the optimal minimax quantizer with the knowledge of the first two moments are determined. The proposed estimators are shown to be consistent even when local sensor noises are not independent but -dependent. The relationship between the proposed approaches and dithering in quantization is also investigated. The superiority of the proposed quantization/estimation schemes is illustrated via illustrative examples.