We study the problem of quantization for distributed parameter estimation in large scale sensor networks. Assuming a Maximum Likelihood estimator at the fusion center, we show that the Fisher Information is maximized by a score-D 2 function quantizer. This provides a tight bound on best possible MSE for any unbiased estimator. Furthermore, we Y. show that for a general convex metric, the optimal quantizer belongs to the class of score function quantizers. We also discuss a few practical applications of our results in optif(Y, Y.) mizing estimation performance in distributed and temporal estimation problems. 0
In an earlier work REF , we had shown the optimality of Score-Function Quantizers in maximizing Fisher Information for a particular θ.
Quantization For Distributed Estimation in Large Scale Sensor Networks
2005 3rd International Conference on Intelligent Sensing and Information Processing
2005 3rd International Conference on Intelligent Sensing and Information Processing
Mathematics