Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Matrix computations (3rd ed.)
Power constrained distributed estimation with cluster-based sensor collaboration
IEEE Transactions on Wireless Communications
Hyperplane-based vector quantization for distributed estimation in wireless sensor networks
IEEE Transactions on Information Theory
Optimal dimensionality reduction of sensor data in multisensor estimation fusion
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing - Part II
Distributed Estimation Using Reduced-Dimensionality Sensor Observations
IEEE Transactions on Signal Processing
Rate-Constrained Distributed Estimation in Wireless Sensor Networks
IEEE Transactions on Signal Processing
Quantization for Maximin ARE in Distributed Estimation
IEEE Transactions on Signal Processing - Part II
Bandwidth-constrained distributed estimation for wireless sensor Networks-part I: Gaussian case
IEEE Transactions on Signal Processing
Universal decentralized detection in a bandwidth-constrained sensor network
IEEE Transactions on Signal Processing - Part I
Linear Coherent Decentralized Estimation
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Sensors' optimal dimensionality compression matrix in estimation fusion
Automatica (Journal of IFAC)
Decentralized estimation in an inhomogeneous sensing environment
IEEE Transactions on Information Theory
The Distributed Karhunen–Loève Transform
IEEE Transactions on Information Theory
Dimensionality Reduction for Distributed Estimation in the Infinite Dimensional Regime
IEEE Transactions on Information Theory
IEEE Communications Magazine
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We consider distributed estimation of a deterministic vector parameter from noisy sensor observations in a wireless sensor network (WSN). The observation noise is assumed uncorrelated across sensors. To meet stringent power and bandwidth budgets inherent in WSNs, local data dimensionality reduction is performed at each sensor to reduce the number of messages sent to a fusion center (FC). The problem of interest is to jointly design the compression matrices associated with those sensors, aiming at minimizing the estimation error at the FC. Such a dimensionality reduction problem is investigated in this paper. Specifically, we study a homogeneous environment where all sensors have identical noise covariance matrices and an inhomogeneous environment where the noise covariance matrices across the sensors have the same correlation structure but with different scaling factors. Given a total number of messages sent to the FC, theoretical lower bounds on the estimation error of any compression strategy are derived for both cases. Compression strategies are developed to approach or even attain the corresponding theoretical lower bounds. Performance analysis and simulations are carried out to illustrate the optimality and effectiveness of the proposed compression strategies.