Real and complex analysis, 3rd ed.
Real and complex analysis, 3rd ed.
MiniMax Methods for Image Reconstruction
MiniMax Methods for Image Reconstruction
Probability and Computing: Randomized Algorithms and Probabilistic Analysis
Probability and Computing: Randomized Algorithms and Probabilistic Analysis
Matched source-channel communication for field estimation in wireless sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
All of Nonparametric Statistics (Springer Texts in Statistics)
All of Nonparametric Statistics (Springer Texts in Statistics)
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
Source-channel communication in sensor networks
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Robust Distributed Estimation Using the Embedded Subgraphs Algorithm
IEEE Transactions on Signal Processing
Distributed Estimation and Detection for Sensor Networks Using Hidden Markov Random Field Models
IEEE Transactions on Signal Processing
The CEO problem [multiterminal source coding]
IEEE Transactions on Information Theory
The quadratic Gaussian CEO problem
IEEE Transactions on Information Theory
Resilience properties of redundant expansions under additive noise and quantization
IEEE Transactions on Information Theory
To code, or not to code: lossy source-channel communication revisited
IEEE Transactions on Information Theory
Universal decentralized estimation in a bandwidth constrained sensor network
IEEE Transactions on Information Theory
Estimating inhomogeneous fields using wireless sensor networks
IEEE Journal on Selected Areas in Communications
| Hi-index | 35.68 |
The reconstruction of a bounded deterministic field from binary quantized observations of sensors which are randomly deployed over the field domain is studied. The sensor observations are corrupted by bounded additive noise. The study focuses on the extremes of lack of deterministic control in the sensor deployment, lack of knowledge of the noise distribution, and lack of sensing precision and reliability. Such adverse conditions are motivated by possible real-world scenarios where a large collection of low-cost, crudely manufactured sensors are mass-deployed in an environment where little can be assumed about the ambient noise. A simple estimator that reconstructs the entire field from these unreliable, binary-quantized, noisy observations is proposed. Technical conditions for the almost sure and mean squared error (MSE) convergence of the estimate to the field, as die number of sensors tends to infinity, are derived and their implications are discussed. For finite-dimensional, bounded-variation, and Sobolev-differentiable function classes, specific MSE decay rates are derived.