Decentralized Maximum-Likelihood Estimation for Sensor Networks Composed of Nonlinearly Coupled Dynamical Systems Export

@article{BarbarossaDecentralizedSensorSmartgrid07, abstract = {In this paper, we propose a decentralized sensor network scheme capable to reach a globally optimum maximum-likelihood (ML) estimate through self-synchronization of nonlinearly coupled dynamical systems. Each node of the network is composed of a sensor and a first-order dynamical system initialized with the local measurements. Nearby nodes interact with each other exchanging their state value, and the final estimate is associated to the state derivative of each dynamical system. We derive the conditions on the coupling mechanism guaranteeing that, if the network observes one common phenomenon, each node converges to the globally optimal ML estimate. We prove that the synchronized state is globally asymptotically stable if the coupling strength exceeds a given threshold. Acting on a single parameter, the coupling strength, we show how, in the case of nonlinear coupling, the network behavior can switch from a global consensus system to a spatial clustering system. Finally, we show the effect of the network topology on the scalability properties of the network, and we validate our theoretical findings with simulation results.}, author = {Barbarossa, S. and Scutari, G.}, citeulike-article-id = {5983066}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/TSP.2007.893921}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4244647}, day = {18}, doi = {10.1109/TSP.2007.893921}, journal = {Signal Processing, IEEE Transactions on}, keywords = {consensus, pj\_esoa, smart\_grid}, month = {June}, number = {7}, pages = {3456--3470}, posted-at = {2009-10-21 21:24:42}, priority = {2}, title = {Decentralized Maximum-Likelihood Estimation for Sensor Networks Composed of Nonlinearly Coupled Dynamical Systems}, url = {http://dx.doi.org/10.1109/TSP.2007.893921}, volume = {55}, year = {2007} }

TY - JOUR ID - BarbarossaDecentralizedSensorSmartgrid07 L3 - citeulike-article-id:5983066 N2 - In this paper, we propose a decentralized sensor network scheme capable to reach a globally optimum maximum-likelihood (ML) estimate through self-synchronization of nonlinearly coupled dynamical systems. Each node of the network is composed of a sensor and a first-order dynamical system initialized with the local measurements. Nearby nodes interact with each other exchanging their state value, and the final estimate is associated to the state derivative of each dynamical system. We derive the conditions on the coupling mechanism guaranteeing that, if the network observes one common phenomenon, each node converges to the globally optimal ML estimate. We prove that the synchronized state is globally asymptotically stable if the coupling strength exceeds a given threshold. Acting on a single parameter, the coupling strength, we show how, in the case of nonlinear coupling, the network behavior can switch from a global consensus system to a spatial clustering system. Finally, we show the effect of the network topology on the scalability properties of the network, and we validate our theoretical findings with simulation results. IS - 7 JF - Signal Processing, IEEE Transactions on EP - 3470 TI - Decentralized Maximum-Likelihood Estimation for Sensor Networks Composed of Nonlinearly Coupled Dynamical Systems VL - 55 SP - 3456 KW - consensus KW - pj_esoa KW - smart_grid AU - Barbarossa, S AU - Scutari, G PY - 2007/06/18/ UR - http://dx.doi.org/10.1109/TSP.2007.893921 ER -