Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
An introduction to signal detection and estimation (2nd ed.)
An introduction to signal detection and estimation (2nd ed.)
Statistical Digital Signal Processing and Modeling
Statistical Digital Signal Processing and Modeling
Convex Optimization
Multivariate Nonnegative Quadratic Mappings
SIAM Journal on Optimization
Sensor Selection for Minimizing Worst-Case Prediction Error
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
Cutting-set methods for robust convex optimization with pessimizing oracles
Optimization Methods & Software
Optimal sensor selection in binary heterogeneous sensor networks
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Sensor selection via convex optimization
IEEE Transactions on Signal Processing
Signal recovery with cost-constrained measurements
IEEE Transactions on Signal Processing
Maximum set estimators with bounded estimation error
IEEE Transactions on Signal Processing - Part II
Robust mean-squared error estimation in the presence of model uncertainties
IEEE Transactions on Signal Processing
Rate-Constrained Distributed Estimation in Wireless Sensor Networks
IEEE Transactions on Signal Processing
Bandwidth-constrained distributed estimation for wireless sensor Networks-part I: Gaussian case
IEEE Transactions on Signal Processing
Estimation Diversity and Energy Efficiency in Distributed Sensing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing - Part I
Power scheduling of universal decentralized estimation in sensor networks
IEEE Transactions on Signal Processing
Constrained Decentralized Estimation Over Noisy Channels for Sensor Networks
IEEE Transactions on Signal Processing
A proof of the Fisher information inequality via a data processing argument
IEEE Transactions on Information Theory
Information theoretic inequalities
IEEE Transactions on Information Theory
Energy-efficient detection in sensor networks
IEEE Journal on Selected Areas in Communications
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Novel convex measurement cost minimization problems are proposed based on various estimation accuracy constraints for a linear system subject to additive Gaussian noise. Closed form solutions are obtained in the case of an invertible system matrix. In addition, the effects of system matrix uncertainty are studied both from a generic perspective and by employing a specific uncertainty model. The results are extended to the Bayesian estimation framework by treating the unknown parameters as Gaussian distributed random variables. Numerical examples are presented to discuss the theoretical results in detail.