Diffusion Strategies for Distributed Kalman Filtering and Smoothing

By Federico S. Cattivelli, Ali H. Sayed. IEEE Transactions on Automatic Control, 2010

843 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Mechanics, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/baf83fa0a09085c7b0ed76c99f6e9a5993f05035

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Citations in the corpus

Exact Diffusion for Distributed Optimization and Learning—Part I: Algorithm Development (2017, background): “Policy 3 (Relative-degree rule): The third policy we consider is the relative-degree combination rule [49] where a k is chosen as”
Distributed Online Optimization in Dynamic Environments Using Mirror Descent (2016, methodology): “This problem has been studied in the context of distributed Kalman filtering [23], [42], state estimation [43]–[45], and particle filtering [24], [46], [47].”
Distributed Event-Triggered Estimation Over Sensor Networks: A Survey (2020, background): “Such a distributed consensus estimation strategy is particularly useful for computing averages as the fundamental objective is that all sensors should guarantee the same estimate in steady state [49]; 5) different from the estimators (4), (5), (9), and (10), the estimators (6)–(8) and (11), also kno”
A Survey on Model-Based Distributed Control and Filtering for Industrial Cyber-Physical Systems (2019, methodology): “The diffusion Kalman ﬁltering is developed in [18] by replacing consensus strategies [12] with diffusion strategies [19] after the measurement update of the Kalman ﬁlter.”
A Dynamic Event-Triggered Transmission Scheme for Distributed Set-Membership Estimation Over Wireless Sensor Networks (2019, neutral): “In contrast, the problem of distributed Kalman filtering and smoothing was addressed in [4] based on diffusion strategies, where sensor information is diffused across the network through a sequence of Kalman iterations and data aggregation.”
Adaptive Networks (2014, background): “The two listings below from [11] show two equivalent forms of a diffusion strategy proposed in [116], [176], and [177] to evaluate approximate predicted and filtered versions of these local estimators in a distributed manner.”
Stability of consensus extended Kalman filter for distributed state estimation (2016, methodology): “The literature on the subject is quite vast and includes approaches based on consensus Kalman filtering (Olfati-Saber, 2007; Kamgarpour and Tomlin, 2008; Kar and Moura, 2011; Cattivelli and Sayed, 2010; Li and Jia, 2012; Zhou et al., 2013; Wang et al., 2014), Luenberger-like consensus estimation (St”
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 (”
Adaptation, Learning, and Optimization over Networks (2014, background): “64) employs a convex combination of the estimators {ψ ,i} with generally different weights {a k} for different neighbors [53, 54].”
Diffusion LMS Strategies for Distributed Estimation (2010, methodology): “These include incremental LMS [3], [5], [6], incremental RLS [3],diffusionLMS[3],[7],anddiffusionRLS[8],[9].Diffusion Kalman filtering and smoothing algorithms were also proposed [10], [11], [ 32 ].”
Diffusion Adaptation Strategies for Distributed Optimization and Learning Over Networks (2011, positive): “Since the {cl,k} are all nonnegative and each Jl(w) is convex, then J lock (w) is also a convex function (actually, the J loc k (w) will be guaranteed to be strongly convex in our treatment in view of Assumption 1 further ahead).”
Kullback-Leibler average, consensus on probability densities, and distributed state estimation with guaranteed stability (2014, methodology): “In fact, the existing approaches can be mainly divided into two families: in the first group (Cattivelli & Sayed, 2010; OlfatiSaber, 2009; Stankovic et al., 2009), a consensus among the network nodes is seeked by averaging out only the state estimates, thus disregarding the covariancematrices…”
Diffusion strategies for adaptation and learning over networks: an examination of distributed strategies and network behavior (2013, methodology): “We can also apply diffusion strategies to solve recursive least-squares and state-space estimation problems in a distributed manner [84], [36]; consensus-based solutions for state-space estimation appear in [85] and [86].”
Diffusion Adaptation over Networks (2012, methodology): “I of [18] and the matrix M in Lemma 2 of [33] are block diagonal, the ‖ · ‖b,∞ norm should replace the ‖ · ‖ρ norm used there, as in the proof that led to (606) and as already done in [54].”