Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Regularization theory and neural networks architectures
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
Distributed cooperative Bayesian learning strategies
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
Distributed Detection and Data Fusion
Distributed Detection and Data Fusion
Parallel and Distributed Computation: Numerical Methods
Parallel and Distributed Computation: Numerical Methods
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Distributed regression: an efficient framework for modeling sensor network data
Proceedings of the 3rd international symposium on Information processing in sensor networks
Robust distributed estimation in sensor networks using the embedded polygons algorithm
Proceedings of the 3rd international symposium on Information processing in sensor networks
Learning the Kernel Function via Regularization
The Journal of Machine Learning Research
Inference in sensor networks: graphical models and particle methods
Inference in sensor networks: graphical models and particle methods
The Journal of Machine Learning Research
Bayes and empirical Bayes semi-blind deconvolution using eigenfunctions of a prior covariance
Automatica (Journal of IFAC)
Distributed algorithms for reaching consensus on general functions
Automatica (Journal of IFAC)
Distributed learning and cooperative control for multi-agent systems
Automatica (Journal of IFAC)
A collaborative training algorithm for distributed learning
IEEE Transactions on Information Theory
Distributed estimation over an adaptive incremental network based on the affine projection algorithm
IEEE Transactions on Signal Processing
Distributed sparse linear regression
IEEE Transactions on Signal Processing
Consensus in Ad Hoc WSNs With Noisy Links—Part I: Distributed Estimation of Deterministic Signals
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
IEEE Communications Magazine
Randomized consensus algorithms over large scale networks
IEEE Journal on Selected Areas in Communications
Mobile Sensor Network Navigation Using Gaussian Processes With Truncated Observations
IEEE Transactions on Robotics
Automatica (Journal of IFAC)
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In this paper we focus on collaborative multi-agent systems, where agents are distributed over a region of interest and collaborate to achieve a common estimation goal. In particular, we introduce two consensus-based distributed linear estimators. The first one is designed for a Bayesian scenario, where an unknown common finite-dimensional parameter vector has to be reconstructed, while the second one regards the nonparametric reconstruction of an unknown function sampled at different locations by the sensors. Both of the algorithms are characterized in terms of the trade-off between estimation performance, communication, computation and memory complexity. In the finite-dimensional setting, we derive mild sufficient conditions which ensure that a distributed estimator performs better than the local optimal ones in terms of estimation error variance. In the nonparametric setting, we introduce an on-line algorithm that allows the agents to simultaneously compute the function estimate with small computational, communication and data storage efforts, as well as to quantify its distance from the centralized estimate given by a Regularization Network, one of the most powerful regularized kernel methods. These results are obtained by deriving bounds on the estimation error that provide insights on how the uncertainty inherent in a sensor network, such as imperfect knowledge on the number of agents and the measurement models used by the sensors, can degrade the performance of the estimation process. Numerical experiments are included to support the theoretical findings.