Kalman-Consensus Filter : Optimality, stability, and performance
By Reza Olfati-Saber. IEEE Conference on Decision and Control, 2009
646 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Networks and Communications
View Paper: https://www.semanticscholar.org/paper/887e48278e922b1c258ea14d088c3c867fb1a527
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Qing-Long Han
- Derui Ding
- Xiaohua Ge
- Giorgio Battistelli
- Luigi Chisci
- Zidong Wang
- Jianshu Chen
- Ali H. Sayed
In Computer Networks and Communications
- Qing-Long Han
- Ali H. Sayed
- Derui Ding
- Xiaohua Ge
- Zidong Wang
- Jianshu Chen
- Giorgio Battistelli
- Luigi Chisci
Citations in the corpus
- 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].”
- Adaptive Networks (2014, background): “Distributed strategies can also be applied to the solution of state–space filtering and smoothing problems [82], [91], [109], [116], [174]–[179].”
- Information Weighted Consensus Filters and Their Application in Distributed Camera Networks (2013, methodology): “The Kalman Consensus Filter (KCF) [4] is a popular and efficient algorithm to solve this task in a distributed framework.”
- A Survey on Model-Based Distributed Control and Filtering for Industrial Cyber-Physical Systems (2019, methodology): “Calculate x̂i,k via (1); 2: Consensus error calculation 3: Communications Broadcast x̂i,k−1 and P− ij,k to all neighbors; Receive x̂j,k−1 and P− j i,k from all neighbors; 4: Gain calculations via similar formulas in [22] Select Fi,k via a similar formula (18); Calculate Ki,k and Pij,k via a similar ”
- Adaptation, Learning, and Optimization over Networks (2014, background): “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].”
- Kullback-Leibler average, consensus on probability densities, and distributed state estimation with guaranteed stability (2014, methodology): “Consensus filters provide a general tool for distributed averaging; in the context of distributed state estimation, they have been exploited for averaging innovations (Kamgarpour & Tomlin, 2008; OlfatiSaber, 2007) or state estimates (Alriksson & Rantzer, 2006; Carli, Chiuso, Schenato, & Zampieri, 20”