Hopfield neural networks for on-line parameter estimation
Neural Networks
Polynomial filtering for fast convergence in distributed consensus
IEEE Transactions on Signal Processing
Network lifetime maximization for estimation in multihop wireless sensor networks
IEEE Transactions on Signal Processing
Distributed LMS for consensus-based in-network adaptive processing
IEEE Transactions on Signal Processing
Distributed in-network channel decoding
IEEE Transactions on Signal Processing
Energy planning for progressive estimation in multihop sensor networks
IEEE Transactions on Signal Processing
Decentralized localization for dynamic and sparse robot networks
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Quantization, channel compensation, and energy allocation for estimation in wireless sensor networks
WiOPT'09 Proceedings of the 7th international conference on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks
Distributed recursive least-squares for consensus-based in-network adaptive estimation
IEEE Transactions on Signal Processing
Fast distributed average consensus algorithms based on advection-diffusion processes
IEEE Transactions on Signal Processing
Distributed consensus with quantized data via sequence averaging
IEEE Transactions on Signal Processing
Adaptive fast consensus algorithm for distributed sensor fusion
Signal Processing
IEEE Transactions on Robotics
Distributed spectrum sensing for cognitive radio networks by exploiting sparsity
IEEE Transactions on Signal Processing
Reaching consensus in wireless networks with probabilistic broadcast
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Performance analysis of the consensus-based distributed LMS algorithm
EURASIP Journal on Advances in Signal Processing
Fusion and diversity trade-offs in cooperative estimation over fading channels
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Distributed consensus-based demodulation: algorithms and error analysis
IEEE Transactions on Wireless Communications
Stochastic consensus over noisy networks with Markovian and arbitrary switches
Automatica (Journal of IFAC)
Convergence of consensus models with stochastic disturbances
IEEE Transactions on Information Theory
Weight optimization for consensus algorithms with correlated switching topology
IEEE Transactions on Signal Processing
Decentralized sparse signal recovery for compressive sleeping wireless sensor networks
IEEE Transactions on Signal Processing
Estimating multiple frequency-hopping signal parameters via sparse linear regression
IEEE Transactions on Signal Processing
Distributed sparse linear regression
IEEE Transactions on Signal Processing
Binary consensus over fading channels
IEEE Transactions on Signal Processing
Low-power distributed Kalman filter for wireless sensor networks
EURASIP Journal on Embedded Systems
Quantization, channel compensation, and optimal energy allocation for estimation in sensor networks
ACM Transactions on Sensor Networks (TOSN)
Foundations and Trends® in Machine Learning
Distributed estimation via iterative projections with application to power network monitoring
Automatica (Journal of IFAC)
Multi-rate distributed fusion estimation for sensor networks with packet losses
Automatica (Journal of IFAC)
Distributed parametric and nonparametric regression with on-line performance bounds computation
Automatica (Journal of IFAC)
Distributed static linear Gaussian models using consensus
Neural Networks
Optimal decentralized Kalman filter and Lainiotis filter
Digital Signal Processing
Hi-index | 35.80 |
We deal with distributed estimation of deterministic vector parameters using ad hoc wireless sensor networks (WSNs). We cast the decentralized estimation problem as the solution of multiple constrained convex optimization subproblems. Using the method of multipliers in conjunction with a block coordinate descent approach we demonstrate how the resultant algorithm can be decomposed into a set of simpler tasks suitable for distributed implementation. Different from existing alternatives, our approach does not require the centralized estimator to be expressible in a separable closed form in terms of averages, thus allowing for decentralized computation even of nonlinear estimators, including maximum likelihood estimators (MLE) in nonlinear and non-Gaussian data models. We prove that these algorithms have guaranteed convergence to the desired estimator when the sensor links are assumed ideal. Furthermore, our decentralized algorithms exhibit resilience in the presence of receiver and/or quantization noise. In particular, we introduce a decentralized scheme for least-squares and best linear unbiased estimation (BLUE) and establish its convergence in the presence of communication noise. Our algorithms also exhibit potential for higher convergence rate with respect to existing schemes. Corroborating simulations demonstrate the merits of the novel distributed estimation algorithms.