Distributed Compression-Estimation Using Wireless Sensor Networks

Abstract

In this paper we consider deterministic parameter estimation problems. We study the intertwining of quantization and estimation in general and shows particular results in 1) low SNR situations where the noise standard deviation is in the order of the parameter's dynamic range; and 2) universal estimation when the sensor data and noise model are unknown. The goal is to understand how the signal processing capability of a WSN scales up with its size, and to develop robust distributed signal processing algorithms and protocols with low bandwidth requirement and optimal performance. We show that for universal estimation in low signal to noise ratio (SNR), the universal distributed estimators not only exist but achieve performance close to that of estimators based on the original (un-quantized) observations. We also generalize these results a Bayesian estimation framework with a particular application to state estimation of dynamic stochastic processes.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2006
Accession Number
ADA455483

Entities

People

  • Alejandro Ribeiro
  • Georgios B. Giannakis
  • Jin-jun Xiao
  • Zhi-quan Luo

Organizations

  • University of Minnesota

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Channel Coding
  • Communication Channels
  • Detectors
  • Electrical Engineering
  • Estimators
  • Information Processing
  • Information Theory
  • Multiple Access
  • Operations Research
  • Probability
  • Sensor Networks
  • Signal Processing
  • Stochastic Processes
  • Theorems
  • Wireless Communications
  • Wireless Sensor Networks

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computer Networking

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms