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
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
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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Full precision Cramér-Rao lower bound (CRLB) where no quantization is assumed is often employed to evaluate and compare distributed estimation performance even though the sensor observations are quantized before any further processing. However, as it completely disregards quantization and often does not exist when the sensor observation noise is bounded, full precision CRLB is often too optimistic or not applicable. In this work, we determine the performance limit of a distributed estimation system with identical one-bit quantizers in terms of the metric minimax CRLB. The performance limit that a distributed estimation scheme with identical quantizers can achieve is found as well as the set of optimal noise distribution functions and quantizers. Compared to the full precision CRLB, the performance limit is shown to be a much tighter bound when the parameter range is relatively large and reveals the important role of the quantization system.