Adaptive algorithms and stochastic approximations
Adaptive algorithms and stochastic approximations
Vector quantization and signal compression
Vector quantization and signal compression
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking
Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking
Posterior Cramer-Rao bounds for discrete-time nonlinear filtering
IEEE Transactions on Signal Processing
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
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This paper addresses a problem of location parameter estimation from multibit quantized measurements. An adaptive estimation algorithm using an adjustable quantizer is proposed. By using general results from adaptive algorithms theory, the asymptotic estimation performance is obtained and optimized through the quantizer parameters. Despite its very low complexity, it can be shown that the proposed algorithm is asymptotically optimal for estimating a constant parameter. The asymptotic performance for optimal quantizer parameters is shown to rapidly reach real-valued based estimation performance as the number of bits increases. In practice, 4-bit quantization appears to be enough for estimation purposes. It is also shown that the performance gap between the quantized and continuous cases is even smaller when the parameter varies according to a random walk (Discrete Wiener process with or without drift).