Distributed sensor localization in random environments using minimal number of anchor nodes
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Cooperative network localization via node velocity estimation
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
Distributed Kalman Filter algorithms for self-localization of mobile devices
Proceedings of the 13th ACM international conference on Hybrid systems: computation and control
Higher dimensional consensus: learning in large-scale networks
IEEE Transactions on Signal Processing
Diffusion distributed Kalman filtering with adaptive weights
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Modeling of future cyber-physical energy systems for distributed sensing and control
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Consensus target tracking in multi-robot systems
ICIRA'10 Proceedings of the Third international conference on Intelligent robotics and applications - Volume Part I
Low-power distributed Kalman filter for wireless sensor networks
EURASIP Journal on Embedded Systems
Optimal decentralized Kalman filter and Lainiotis filter
Digital Signal Processing
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This paper presents a distributed Kalman filter to estimate the state of a sparsely connected, large-scale, n -dimensional, dynamical system monitored by a network of N sensors. Local Kalman filters are implemented on nl-dimensional subsystems, nl Lt n, obtained by spatially decomposing the large-scale system. The distributed Kalman filter is optimal under an Lth order Gauss-Markov approximation to the centralized filter. We quantify the information loss due to this Lth-order approximation by the divergence, which decreases as L increases. The order of the approximation L leads to a bound on the dimension of the subsystems, hence, providing a criterion for subsystem selection. The (approximated) centralized Riccati and Lyapunov equations are computed iteratively with only local communication and low-order computation by a distributed iterate collapse inversion (DICI) algorithm. We fuse the observations that are common among the local Kalman filters using bipartite fusion graphs and consensus averaging algorithms. The proposed algorithm achieves full distribution of the Kalman filter. Nowhere in the network, storage, communication, or computation of n-dimensional vectors and matrices is required; only nl Lt n dimensional vectors and matrices are communicated or used in the local computations at the sensors. In other words, knowledge of the state is itself distributed.