Adaptive signal processing algorithms: stability and performance
Adaptive signal processing algorithms: stability and performance
Feedback Control of Dynamic Systems
Feedback Control of Dynamic Systems
Parallel and Distributed Computation: Numerical Methods
Parallel and Distributed Computation: Numerical Methods
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
A space-time diffusion scheme for peer-to-peer least-squares estimation
Proceedings of the 5th international conference on Information processing in sensor networks
Adaptive Filters
Diffusion Least-Mean Squares Over Adaptive Networks: Formulation and Performance Analysis
IEEE Transactions on Signal Processing - Part II
Incremental Adaptive Strategies Over Distributed Networks
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 Signal Processing
IEEE Transactions on Signal Processing
The capacity of wireless networks
IEEE Transactions on Information Theory
Distributed recursive least-squares for consensus-based in-network adaptive estimation
IEEE Transactions on Signal Processing
Diffusion LMS strategies for distributed estimation
IEEE Transactions on Signal Processing
Performance analysis of the consensus-based distributed LMS algorithm
EURASIP Journal on Advances in Signal Processing
Decentralized sparse signal recovery for compressive sleeping wireless sensor networks
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)
Optimal decentralized Kalman filter and Lainiotis filter
Digital Signal Processing
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Adaptive algorithms based on in-network processing of distributed observations are well-motivated for online parameter estimatiop and tracking of (non)stationary signals using ad hoc wireless sensor networks (WSNs). To this end, a fully distributed least mean-square (D-LMS) algorithm is developed in this paper, offering simplicity and flexibility while solely requiring single-hop communications among sensors. The resultant estimator minimizes a pertinent squared-error cost by resorting to i) the alternating-direction method of multipliers so as to gain the desired degree of parallelization and ii) a stochastic approximation iteration to cope with the time-varying statistics of the process under consideration. Information is efficiently percolated across the WSN using a subset of "bridge" sensors, which further tradeoff communication cost for robustness to sensor failures. For a linear data model and under mild assumptions aligned with those considered in the centralized LMS, stability of the novel D-LMS algorithm is established to guarantee that local sensor estimation error norms remain bounded most of the time. Interestingly, this weak stochastic stability result extends to the pragmatic setup where intersensor communications are corrupted by additive noise. In the absence of observation and communication noise, consensus is achieved almost surely as local estimates are shown exponentially convergent to the parameter of interest with probability one. Mean-square error performance of D-LMS is also assessed. Numerical simulations: i) illustrate that D-LMS outperforms existing alternatives that rely either on information cliffusion among neighboring sensors, or, local sensor filtering; ii) highlight its tracking capabilities; and iii) corroborate the stability and performance analysis results.