An adaptive projected subgradient approach to learning in diffusion networks
IEEE Transactions on Signal Processing
Distributed LMS for consensus-based in-network adaptive processing
IEEE Transactions on Signal Processing
Energy planning for progressive estimation in multihop sensor networks
IEEE Transactions on Signal Processing
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Distributed recursive least-squares for consensus-based in-network adaptive estimation
IEEE Transactions on Signal Processing
Adaptive fast consensus algorithm for distributed sensor fusion
Signal Processing
Diffusion LMS strategies for distributed estimation
IEEE Transactions on Signal Processing
Adaptive filter algorithms for accelerated discrete-time consensus
IEEE Transactions on Signal Processing
Performance analysis of the consensus-based distributed LMS algorithm
EURASIP Journal on Advances in Signal Processing
Distributed estimation over an adaptive incremental network based on the affine projection algorithm
IEEE Transactions on Signal Processing
Distributed estimation of channel gains in wireless sensor networks
IEEE Transactions on Signal Processing
Diffusion LMS-based distributed detection over adaptive networks
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Mean-square convergence analysis of ADALINE training with minimum error entropy criterion
IEEE Transactions on Neural Networks
Diffusion least-mean squares with adaptive combiners: formulation and performance analysis
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Distributed estimation over complex networks
Information Sciences: an International Journal
Distributed estimation via iterative projections with application to power network monitoring
Automatica (Journal of IFAC)
Digital Signal Processing
Optimal topological design for distributed estimation over sensor networks
Information Sciences: an International Journal
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We formulate and study distributed estimation algorithms based on diffusion protocols to implement cooperation among individual adaptive nodes. The individual nodes are equipped with local learning abilities. They derive local estimates for the parameter of interest and share information with their neighbors only, giving rise to peer-to-peer protocols. The resulting algorithm is distributed, cooperative and able to respond in real time to changes in the environment. It improves performance in terms of transient and steady-state mean-square error, as compared with traditional noncooperative schemes. Closed-form expressions that describe the network performance in terms of mean-square error quantities are derived, presenting a very good match with simulations.