Bandwidth-constrained distributed estimation for wireless sensor Networks-part I: Gaussian case

We study deterministic mean-location parameter estimation when only quantized versions of the original observations are available, due to bandwidth constraints. When the dynamic range of the parameter is small or comparable with the noise variance, we introduce a class of maximum-likelihood estimato...

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Bibliographic Details
Published in:IEEE transactions on signal processing Vol. 54; no. 3; pp. 1131 - 1143
Main Authors: Ribeiro, A., Giannakis, G.B.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.03.2006
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Additive white noise
Algorithms
Applied sciences
Bandwidth
Collaborative work
Computer aided software engineering
Detection, estimation, filtering, equalization, prediction
Dynamic range
Estimators
Exact sciences and technology
Information, signal and communications theory
Maximization
Maximum likelihood estimation
Noise
Parameter estimation
Quantization
Sampling, quantization
Sensor phenomena and characterization
Sensors
Services and terminals of telecommunications
Signal and communications theory
Signal, noise
Systems, networks and services of telecommunications
Telecommunications
Telecommunications and information theory
Telemetry. Remote supervision. Telewarning. Remote control
Variance
Wireless sensor networks
Availability
Parameter estimation
wireless sensor networks
Wireless telecommunication
Location parameter
Algorithm
Mean estimation
Implementation
Remote sensing
Signal quantization
Standard deviation
Maximum likelihood
Resource management
Localization
Sensor array
Deterministic approach
ISSN:1053-587X, 1941-0476
Online Access:Get full text
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Description
Summary:We study deterministic mean-location parameter estimation when only quantized versions of the original observations are available, due to bandwidth constraints. When the dynamic range of the parameter is small or comparable with the noise variance, we introduce a class of maximum-likelihood estimators that require transmitting just one bit per sensor to achieve an estimation variance close to that of the (clairvoyant) sample mean estimator. When the dynamic range is comparable or larger than the noise standard deviation, we show that an optimum quantization step exists to achieve the best possible variance for a given bandwidth constraint. We will also establish that in certain cases the sample mean estimator formed by quantized observations is preferable for complexity reasons. We finally touch upon algorithm implementation issues and guarantee that all the numerical maximizations required by the proposed estimators are concave.
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ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2005.863009