Matrix analysis
Optimization theory with applications
Optimization theory with applications
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Matrix computations (3rd ed.)
Convex Optimization
Optimal dimensionality reduction of sensor data in multisensor estimation fusion
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing - Part II
IEEE Transactions on Signal Processing
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
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Sequential signal encoding from noisy measurements using quantizers with dynamic bias control
IEEE Transactions on Information Theory
Universal decentralized estimation in a bandwidth constrained sensor network
IEEE Transactions on Information Theory
IEEE Communications Magazine
IEEE Transactions on Signal Processing
Brief paper: An efficient sensor quantization algorithm for decentralized estimation fusion
Automatica (Journal of IFAC)
Robust distributed maximum likelihood estimation with dependent quantized data
Automatica (Journal of IFAC)
Hi-index | 754.84 |
This paper considers distributed estimation of a vector parameter in the presence of zero-mean additive multivariate Gaussian noise in wireless sensor networks. Due to stringent power and bandwidth constraints, vector quantization is performed at each sensor to convert its local noisy vector observation into one bit of information, which is then forwarded to a fusion center where a final estimate of the vector parameter is obtained. Within such a context, this paper focuses on a class of hyperplane-based vector quantizers which linearly convert the observation vector into a scalar by using a compression vector and then carry out a scalar quantization. It is shown that the key of the vector quantization design is to find a compression vector for each sensor. Under the framework of the Cramér-Rao bound (CRB) analysis, the compression vector design problem is formulated as an optimization problem that minimizes the trace of the CRB matrix. Such an optimization problem is extensively studied. In particular, an efficient iterative algorithm is developed for the general case, along with optimal and near-optimal solutions for some specific but important noise scenarios. Performance analysis and simulation results are carried out to illustrate the effectiveness of the proposed scheme.