Consensus algorithm for distributed state estimation in multi-clusters sensor network

Liu, Yu; Li, Jun; Xu, Congan; Lin, Qian; Sun, Shun; Ding, Ziran

doi:10.23919/icif.2017.8009845

Cited by 5 publications

(5 citation statements)

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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%

“…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]. Recently, in [23,24], the multiscale consensus scheme, in which the local estimated states achieve asymptotically prescribed ratios in terms of multiple scales, has been discussed and analyzed.…”

Section: Introductionmentioning

confidence: 99%

“…Secondly, the prior estimate information and measurement information at current time instant within the inclusive neighborhood are, respectively, combined together to form the local generalized prior estimate equation and the local generalized measurement equation. Then, a distributed local MAP estimator is derived with some reasonable approximations of the error covariance matrices, which achieves higher accuracy than the approaches introduce in [4,5,6,11,16,18,19,25,26,27,28]. Finally, the average consensus protocol with the aforementioned consensus weights is incorporated into the estimation framework, and the proposed DHIWCF is obtained.…”

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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Section: Performance Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Distributed State Estimation Algorithm with Consensus Strategy

Liu

Dong

et al. 2019

Sensors

Self Cite

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“…With sufficient consensus iterations, all nodes produce the identical estimates with comparable performance to its centralized counterparts [1], [3]- [5], which makes it possible for each node to make decision independently. Due to its good scalability, low communication burden and robustness to sensor failures [6], [8], [16], the consensusbased techniques are widely applied in the fields such as The associate editor coordinating the review of this article and approving it for publication was Zhen Li. environmental monitoring, intelligent transportation and target tracking [8].…”

Section: Introductionmentioning

confidence: 99%

Distributed High-Degree Cubature Information Filter With Embedded Hybrid Consensus

Liu

Dong

et al. 2019

IEEE Access

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To settle the problem of distributed nonlinear state estimation in sensor networks with naive nodes, a novel distributed high-degree cubature information filter with embedded hybrid consensus (DHCIF) is proposed. The multi-dimensional Gaussian weighted integrals involved in the filtering process are approximated by the fifth-degree cubature rule. A novel scheme, with consideration for the predicted measurement errors, is applied to compute the information contribution. With parallel consensus performed on both prior and measurement information, the proposed DHCIF is derived. The stability analysis with regard to consistency and boundedness of estimation errors for the proposed DHCIF is also developed. Finally, the effectiveness and advantage of the proposed DHCIF is validated by a typical maneuvering target tracing scenario. The experimental results indicate that the proposed DHCIF outperforms the existing algorithms in the aspects of estimation accuracy, consistency and consensus at the cost of a little more extra computation burden.

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