Distributed Kalman Filter with Embedded Consensus Filters

By Reza Olfati-Saber. 2005

1,068 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

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Citations in the corpus

Distributed Kalman filtering for sensor networks (2007, methodology): “Recently in [10], the author introduced a distributed Kalman Filtering (DKF) algorithm that uses dynamic consensus algorithms [14], [20].”
Distributed Consensus Filtering in Sensor Networks (2009, background): “Recently, distributed estimation and control have been shown to be very effective for consensus [7]–[9].”
Information fusion for wireless sensor networks: Methods, models, and classifications (2007, methodology): “In WSNs, we can .nd schemes to approximate distributed Kalman .ltering, in which the solution is computed based on reaching an average consensus among sensor nodes [Spanos et al. 2005; Olfati-Saber 2005].”
Communication constraints in the average consensus problem (2008, background): “The interest in these type of problems is not limited to the field of mobile agents coordination but also involves problems of synchronization [40,26,25] and distributed estimation [29,8].”
Consensus Conditions of Multi-Agent Systems With Time-Varying Topologies and Stochastic Communication Noises (2010, background): “Digital Object Identifier 10.1109/TAC.2010.2042982 [2], multi-senor data fusion [3] and distributed computation [4].”
Kalman-Consensus Filter : Optimality, stability, and performance (2009, methodology): “In [13], the author introduced the first scalable and distributed Kalman Filtering (DKF) algorithm which relied on dynamic average-consensus [20].”
Flocking for multi-agent dynamic systems: algorithms and theory (2006, background): “The self-organizing feature of flocks/schools [12] can provide a deeper insight in design of sensor networks [13]–[16].”
Cooperative Filters and Control for Cooperative Exploration (2010, methodology): “Therefore, the distributed Kalman filter algorithms for tracking and coverage in [26], [28], and [29], which achieve consensus between nodes and increase computational efficiency, are not applicable here.”
Multi-Agent Coordination by Decentralized Estimation and Control (2008, methodology): “Dynamic consensus filters have also been utilized to construct decentralized Kalman filters [23], [30], [41], [47].”
Distributed Consensus With Limited Communication Data Rate (2011, neutral): “2052384 on consensus algorithms ([1], [2]).”
Belief consensus and distributed hypothesis testing in sensor networks (2006, neutral): “signal processing, and control theory [20, 7, 10, 11, 5, 1, 6, 17, 14, 24, 28].”
Adaptation, Learning, and Optimization over Networks (2014, background): “, [8, 32, 52, 83, 128, 138, 139, 143, 175, 176, 180, 225, 243, 266]) proposed useful extensions of the original consensus construction (7.”
Decentralized Environmental Modeling by Mobile Sensor Networks (2008, methodology): “The distributed KF described in [31] uses both the low-pass dynamic consensus filter of [35] and a band-pass consensus filter that composes the low-pass filter with the high-pass average filter of [39].”
Diffusion Strategies for Distributed Kalman Filtering and Smoothing (2010, background): “A second difference between our work and [10], [11] is that the latter are motivated by consensus arguments in continuous time and the filters are subsequently discretized.”
Distributing the Kalman Filter for Large-Scale Systems (2007, methodology): “sensor. Communication requirements are global in [3], and local in [ 7 ].”
Distributed Kalman filtering based on consensus strategies (2008, background): “This approach has been extended to static multivariable systems in [33] and to dynamic multivariable systems in [23][16].”
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.”
Distributed Parameter Estimation in Sensor Networks: Nonlinear Observation Models and Imperfect Communication (2008, background): “, [15], [16], [17], [18], [19], where between measurement updates a large number of consensus steps (theoretically, an infinite number of steps) is taken.”