Distributed Kalman filtering for sensor networks

By Reza Olfati-Saber. 2007

1,558 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

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Citations in the corpus

An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination (2012, neutral): “For example, the authors of [ 43]–[45] designed some distributed Kalman filters to implement data fusion.”
Stability of consensus extended Kalman filter for distributed state estimation (2016, methodology): “The consensus iteration (4) has been originally proposed in [12, 10] in a linear setting.”
A new observer-type consensus protocol for linear multi-agent dynamical systems (2011, background): “This is partly due to its wide applications in sensor networks [5, 6], robotic teams [7, 8], satellite array formation [9], and so on.”
Adaptive Networks (2014, methodology): “The main difference is that the weights fa‘kg in [175] are all defined in terms of a single parameter > 0 and set uniformly to a‘k 1⁄4 for ‘ 2 N k n fkg and akk 1⁄4 1 ðnk 1Þ , whereas the fa‘kg are chosen more arbitrarily in the diffusion setting.”
Distributed model predictive control: A tutorial review and future research directions (2013, positive): “The problem of distributed estimation for sensor networks where each sensor measures just some of the system outputs and computes the estimate of the overall state have been studied in [5], [17], [66], [120] and in [41], [42], [43], where the Moving Horizon Estimation (MHE) approach has been used to”
FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing (2020, methodology): “To meet this challenge, various distributed implementations of the Kalman filter and particle filters, for instance [31, 32, 33, 34, 35], were proposed with similar ideas of approximating the posterior belief/distribution as a product of local posteriors.”
State Estimation in Electric Power Grids: Meeting New Challenges Presented by the Requirements of the Future Grid (2012, background): “IEEE SIGNAL PROCESSING MAGAZINE [33] SEPTEMBER 2012 Digital Object Identifier 10.1109/MSP.2012.2187037 [ Yih-Fang Huang, Stefan Werner, Jing Huang, Neelabh Kashyap, and Vijay Gupta] Date of publication: 20 August 2012 1053-5888/12/$31.00©2012IEEE T his article provides a survey on state estimation (”
Kullback-Leibler average, consensus on probability densities, and distributed state estimation with guaranteed stability (2014, background): “, 2009), a consensus among the network nodes is seeked by averaging out only the state estimates, thus disregarding the covariancematrices aswell as the formof the local PDFs in the nonlinear or non-Gaussian case; the approaches belonging to the second group (Kamgarpour & Tomlin, 2008; Olfati-Saber,”
Distributed Online Optimization in Dynamic Environments Using Mirror Descent (2016, methodology): “The problem setup (1) coupled with the dynamics given in (3) is reminiscent of distributed Kalman filtering [33].”
Kalman-Consensus Filter : Optimality, stability, and performance (2009, methodology): “Simulation results and performance comparisons for the KCF algorithm are provided in [15], [18] and will not be repeated here.”
Distributed H∞-consensus filtering in sensor networks with multiple missing measurements: The finite-horizon case (2010, background): “proposed, see Olfati-Saber and Shamma (2005), Olfati-Saber (2007), Speranzon et al.”
Architectures for distributed and hierarchical Model Predictive Control - A review (2009, neutral): “[77,76,46], where sensor networks have been considered.”
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].”
Adaptation, Learning, and Optimization over Networks (2014, methodology): “Distributed strategies can also be applied to the solution of state-space filtering and smoothing problems [53, 54, 59, 61, 88, 112, 143, 182, 183].”
Distributed recursive filtering for stochastic systems under uniform quantizations and deception attacks through sensor networks (2017, methodology): “In particular, a distributed filtering framework that sheds insightful light on this domain has been established in [25] with the aid of two identical consensus filters for fusion of the sensor data and covariance information.”
Diffusion Strategies for Distributed Kalman Filtering and Smoothing (2010, methodology): “In [11], the author uses a continuous-time”
Consensus-Based Linear and Nonlinear Filtering (2015, methodology): “As an alternative approach, in [12], [13] it has been proposed to exploit consensus in order to compute in a distributed way the quan-”