Distributed Kalman filtering based on consensus strategies

By Ruggero Carli, Alessandro Chiuso, Luca Schenato, Sandro Zampieri. IEEE Journal on Selected Areas in Communications, 2008

509 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/e3a909574d0c2d6c2959a1fef5a1cee0a4904d45

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Citations in the corpus

Kalman-Consensus Filter : Optimality, stability, and performance (2009, result): “Recently, it was revealed in [15] that the performance of the DKF algorithm with embedded averaging consensus filters introduced in [13]—and analyzed by other researchers [6] for an alternative but closely related algorithm—has a relatively weak performance that is comparable with the collective est”
Linear Time Average Consensus on Fixed Graphs and Implications for Decentralized Optimization and Multi-Agent Control (2014, unknown): “Indeed, as examples we mention recent work on coverage control [25], formation control [64, 65], distributed estimation [85, 86, 24, 19, 74], cooperative learning [36], and Kalman filtering [15, 77, 2], distributed task assignment [20], and distributed optimization [82, 57]; these papers and others ”
Distributed Collaborative Control for Industrial Automation With Wireless Sensor and Actuator Networks (2010, methodology): “Distributed estimation methods for use with wireless and distributed networks are studied in [32] and [33].”
Gossip Algorithms for Distributed Signal Processing (2010, background): “, [98]– [102] and the references therein), where the objective is not consensus seeking among the local estimates, but, in general, optimizing fusion strategies to minimize the mean squared error at each sensor.”
Diffusion Strategies for Distributed Kalman Filtering and Smoothing (2010, methodology): “Recent work [13] proposes an efficient algorithm for large-scale distributed Kalman filtering, where the transfer matrices are assumed to be sparse and localized, while [14] considers optimization of the consensus weights for a scalar state-space model.”
Distributing the Kalman Filter for Large-Scale Systems (2007, background): “This section presents the distribution of the global prediction step, (27), into the local prediction step at each LIF.”
Consensus-Based Linear and Nonlinear Filtering (2015, background): “A first family of techniques, hereafter referred to as Consensus on Estimates (CE), is based on the idea of spreading the available information over the network by performing, at each time instant, a consensus averaging of the local state estimates/predictions [4]–[11].”