Optimal discrete-time Kalman Consensus Filter

Deshmukh, Raj; Kwon, Cheolhyeon; Hwang, Inseok

doi:10.23919/acc.2017.7963859

Cited by 28 publications

(24 citation statements)

References 22 publications

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“…With simplicity and optimality among approaches of all types, Kalman filtering is the most popular technique for state estimation [10,16,17,20,[26][27][28][29][30]. It should be mentioned that, only when parameters of the system have no uncertainties, the conventional Kalman filter has the optimal performance.…”

Section: Kalman Filteringmentioning

confidence: 99%

Distributed robust filtering with hybrid consensus strategy for sensor networks

Rastgar

Rahmani

2020

IET wirel. sens. syst.

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The problem of distributed state estimation for time-varying uncertain systems over a sensor network within the robust Kalman filtering framework is studied. It is assumed that the parameters of the underlying model are subjected to stochastic uncertainties. At the first step, the authors present a particular form of the Kalman filter named information form of a robust Kalman filter. Then, using a consensus scheme that guarantees an agreement in estimation among nodes, they propose a new consensus-based robust filtering approach. The consensus methodology is a hybrid method that is a combination of consensus on information (CI) and consensus on measurement (CM). To this end, before proposing the hybrid consensus filtering algorithm, distributed robust filtering based on CI and CM have been presented. Next, they show that the estimation error in the proposed consensus algorithm is exponentially mean square bounded using a Lyapunov function. Finally, they provide two illustrative examples to show the robust performance and effectiveness of the proposed consensus filtering algorithms.

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Section: Kalman Filteringmentioning

confidence: 99%

Distributed robust filtering with hybrid consensus strategy for sensor networks

Rastgar

Rahmani

2020

IET wirel. sens. syst.

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“…The result shown in Theorem 2 is only dependent on collective observability. This is distinct from some algorithms that require some sort of local observability or detectability condition [5,6,8,11,25], which poses a great challenge to the sensing abilities of sensors and restricts the scope of application.…”

Section: Performance Analysismentioning

confidence: 99%

“…Recently, distributed state estimation has been a hot topic in the field of target tracking in sensor networks [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]. As a traditional method, the centralized scheme needs to simultaneously process the local measurements from all sensors in the fusion center at each time instant [3,15].…”

Section: Introductionmentioning

confidence: 99%

“…Unlike the centralized scheme, the distributed mechanism tries to recover the centralized performance via local communications between neighboring nodes. Specifically, each node in the network only exchanges information with its immediate neighbors to achieve a comparable performance to its centralized counterpart, which reduces communication cost and makes the network robust against possible failures of some nodes [8]. The consensus filter, which computes the average of interested values in a distributed manner, has attracted immense popularity for distributed state estimation [4,5,6,7,9,10,11,12,13,14,16,17,18,19,20,21,22].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Distributed State Estimation Algorithm with Consensus Strategy

Liu

Dong

et al. 2019

Sensors

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Owing to its high-fault tolerance and scalability, the consensus-based paradigm has attracted immense popularity for distributed state estimation. If a target is neither observed by a certain node nor by its neighbors, this node is naive about the target. Some existing algorithms have considered the presence of naive nodes, but it takes sufficient consensus iterations for these algorithms to achieve a satisfactory performance. In practical applications, because of constrained energy and communication resources, only a limited number of iterations are allowed and thus the performance of these algorithms will be deteriorated. By fusing the measurements as well as the prior estimates of each node and its neighbors, a local optimal estimate is obtained based on the proposed distributed local maximum a posterior (MAP) estimator. With some approximations of the cross-covariance matrices and a consensus protocol incorporated into the estimation framework, a novel distributed hybrid information weighted consensus filter (DHIWCF) is proposed. Then, theoretical analysis on the guaranteed stability of the proposed DHIWCF is performed. Finally, the effectiveness and superiority of the proposed DHIWCF is evaluated. Simulation results indicate that the proposed DHIWCF can achieve an acceptable estimation performance even with a single consensus iteration.

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“…These distributed estimation applications rely on information exchange across the communication network, where each sensor agent attempts to concur in estimates with its neighbours through communication links. One of the widely used algorithms leveraging this concept of consensus among neighbours [27], is the Kalman consensus filter (KCF) [28, 29] and an optimal KCF has been recently derived in our previous work [30]. These algorithms have a Kalman filter‐like structure, and aim to bring about an agreement or consensus between connected agents regarding the state estimates by exchanging and fusing information packets with the local estimate to improve estimation performance, as compared to local estimation alone.…”

Section: Introductionmentioning

confidence: 99%