Optimal sensor data quantization for best linear unbiased estimation fusion

Parameter Estimation Over Noisy Communication Channels in Distributed Sensor Networks

“…Assuming perfect synchronization among sensor transmissions, the received signal at the fusion center over a MAC can be written as where w is the receiver noise with zero mean and variance of and h k is the channel fading coefficient from node k to the fusion center, as defined earlier. For the AF local processing, substituting y k = g k z k , the resulting received signal is given by (22) Fusion center computes the final estimator based on the received coherent signal r. The resulting BLUE estimator and its performance is given by the following lemma. Lemma 8: [34] The BLUE estimator and the resulting MSE based on the received signal (22) can be shown to be and…”

“…For the AF local processing, substituting y k = g k z k , the resulting received signal is given by (22) Fusion center computes the final estimator based on the received coherent signal r. The resulting BLUE estimator and its performance is given by the following lemma. Lemma 8: [34] The BLUE estimator and the resulting MSE based on the received signal (22) can be shown to be and…”

“…is the final estimator mapping. There are several quantization schemes proposed in the literature each having its own advantages and disadvantages [9], [22], [21]. For simplicity, throughout this chapter we concentrate on the universal decentralized quantization scheme given in [9].…”

Score-Function Quantization for Distributed Estimation

“…In the distributed estimation scenario, Lam and Reibman [3] developed an iterative quantizer to maximize the Bayesian Fisher Information of a random parameter. For certain restrictive types of estimators, Gubner [5] and Zhang and Li [6] provided optimal quantizers to minimize the mean squared error for a random parameter. For a deterministic parameter in additive noise, Ribiero and Giannakis [7] showed that the quantizer that maximizes the Fisher Information at a particular θ is represented by a single threshold in the observation space.…”

Minimax Quantization for Distributed Maximum Likelihood Estimation

“…When the parameter is random, Lam and Reibman [2] developed an iterative quantizer that maximizes the Bayesian Fisher Information. Gubner [3] and Zhang and Li [4] developed quantizers that minimize the MSE of a random parameter for certain restricted classes of estimators. For a deterministic parameter, Luo [5] optimized the number of quantization bits for a uniform quantizer to minimize MSE.…”