A fully decentralized multi-sensor system for tracking and surveillance
International Journal of Robotics Research
Distributed algorithms for reaching consensus on general functions
Automatica (Journal of IFAC)
Bayesian Filtering for Location Estimation
IEEE Pervasive Computing
A distributed minimum variance estimator for sensor networks
IEEE Journal on Selected Areas in Communications
Distributed Kalman filtering based on consensus strategies
IEEE Journal on Selected Areas in Communications
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State estimation is an important topic in the study of dynamical systems. The problem of estimation can be structured into three categories: 1 centralised scheme; 2 decentralised scheme; 3 distributed scheme. Distributed estimation is a compromise between completely centralised and decentralised versions of estimation. In this paper, we will provide an assessment of distributed estimation based on Kalman filtering techniques for large-scale or sensor networks. In simulation, a second order dynamical system is employed in a scenario of ten sensor nodes. The sensor nodes attempt to estimate the states of the dynamical system with embedded consensus filters. The results show that the distributed estimation algorithm effectively approximates the central Kalman filter. It is concluded that the distributed estimation techniques for distributed dynamical system requires further extensive research.