Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Distributed Detection and Data Fusion
Distributed Detection and Data Fusion
Multisensor Decision and Estimation Fusion
Multisensor Decision and Estimation Fusion
Brief paper: State estimation for linear discrete-time systems using quantized measurements
Automatica (Journal of IFAC)
Hyperplane-based vector quantization for distributed estimation in wireless sensor networks
IEEE Transactions on Information Theory
Identification of Hammerstein Systems with Quantized Observations
SIAM Journal on Control and Optimization
IEEE Transactions on Signal Processing
Universal decentralized detection in a bandwidth-constrained sensor network
IEEE Transactions on Signal Processing - Part I
Sequential signal encoding from noisy measurements using quantizers with dynamic bias control
IEEE Transactions on Information Theory
On optimal quantization of noisy sources
IEEE Transactions on Information Theory
Least squares quantization in PCM
IEEE Transactions on Information Theory
Universal decentralized estimation in a bandwidth constrained sensor network
IEEE Transactions on Information Theory
Decentralized estimation in an inhomogeneous sensing environment
IEEE Transactions on Information Theory
Distributed estimation and quantization
IEEE Transactions on Information Theory
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In this paper, we consider the design problem of optimal sensor quantization rules (quantizers) and an optimal linear estimation fusion rule in bandwidth-constrained decentralized random signal estimation fusion systems. First, we derive a fixed-point-type necessary condition for both optimal sensor quantization rules and an optimal linear estimation fusion rule: a fixed point of an integral operation. Then, we can motivate an iterative Gauss-Seidel algorithm to simultaneously search for both optimal sensor quantization rules and an optimal linear estimation fusion rule without Gaussian assumptions on the joint probability density function (pdf) of the estimated parameter and observations. Moreover, we prove that the algorithm converges to a person-by-person optimal solution in the discretized scheme after a finite number of iterations. It is worth noting that the new method can be applied to vector quantization without any modification. Finally, several numerical examples demonstrate the efficiency of our method, and provide some reasonable and meaningful observations how the estimation performance is influenced by the observation noise power and numbers of sensors or quantization levels.