Distributed Robust Kalman Filters under Model Uncertainty and Multiplicative Disturbance

Yu, Xingkai; Li, Jianxun

doi:10.1109/taes.2022.3193344

Cited by 6 publications

(17 citation statements)

References 51 publications

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“…DRAFT 14 φi t ∈ B i t . Thus, the local least favorable transition probability density φi t solution to (23) does not necessarily agree with the global least favorable density φt obtained solving the centralized problem in (5) be the pseudo-nominal and the least favorable conditional probability densities of z t given Y t−1 , respectively. In a similar way we define the pseudo-nominal and the least favorable conditional probability densities of z i t given Y t−1 , respectively, as…”

Section: Discussionmentioning

confidence: 94%

“…In [18] it has been shown that the least favorable density φi t solution to (23) is such that the Kullback-Leibler divergence between f i t and f i t , i.e.…”

Section: Discussionmentioning

confidence: 99%

“…dz i t is equal to the tolerance of the ambiguity set B i t used in (23). Drawing inspiration from [22], it is possible to mitigate the fact that the local least favorable density φi t is different from the one obtained from the global least favorable density φt by considering the ambiguity set B i t about the nominal density φ i t and with "local" tolerance…”

Section: Discussionmentioning

confidence: 99%

“…Although such a choice does not guarantee that φi t , i.e. the least favorable density solution to (23) with tolerance (24), leads to a density f i t which coincides with f i,g t , at least it is ensured that…”

Section: Discussionmentioning

confidence: 99%

“…for instance [19], [20], [21], [22], [23], [24], [25], however, to the best of the authors' knowledge, none of them consider the case with event-triggered communication.…”

Section: Introductionmentioning

confidence: 99%

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Distributed Kalman filtering with event-triggered communication: a robust approach

Ghion

Zorzi

2022

2022 30th Mediterranean Conference on Control and Automation (MED)

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We consider the problem of distributed Kalman filtering for sensor networks in the case there are constraints in data transmission and there is model uncertainty. More precisely, we propose two distributed filtering strategies with event-triggered communication where the state estimators are computed according to the least favorable model. The latter belongs to a ball about the nominal model. We also show that both the methods are stable in the sense that the mean-square of the state estimation error is bounded in all the nodes.

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Section: Discussionmentioning

confidence: 94%

“…In [18] it has been shown that the least favorable density φi t solution to (23) is such that the Kullback-Leibler divergence between f i t and f i t , i.e.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…for instance [19], [20], [21], [22], [23], [24], [25], however, to the best of the authors' knowledge, none of them consider the case with event-triggered communication.…”

Section: Introductionmentioning

confidence: 99%

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Distributed Kalman filtering with event-triggered communication: a robust approach

Ghion

Zorzi

2022

2022 30th Mediterranean Conference on Control and Automation (MED)

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Robust Kalman filters under epistemic uncertainty for non‐Gaussian systems with multiplicative noise

Yu,

Wu,

Xin

et al. 2024

Intl J Robust & Nonlinear

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This article proposes two robust Kalman filters to solve the issue of inaccurate modeling in multiplicative noise systems due to epistemic limitations. First, we construct all conceivable state/measurement transition probability densities as an ambiguity set. This ambiguity set chooses the Wasserstein distance or the moment‐based metric as the distance metric. Besides, this set is an inequality set with a chosen tolerance, which can be seen as a non‐negative radius ball. Then, by combining the robust solution of the least favorable model in that ball with the alternating direction method of multipliers or an efficient direct solution method, we propose two robust Kalman filters based on the minimum mean square error criterion. A classical example is provided to verify the effectiveness of the proposed robust filters in comparison to existing state‐of‐the‐art filters.

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Implementation of extended kalman filter for localization of ambulance robot

Yang,

Samani,

Tang

et al. 2024

Int J Intell Robot Appl

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This paper focuses on the implementation of the Extended Kalman Filter for indoor localization of a semi-autonomous Ambulance Robot system named Ambubot. The system is designed to reduce the response time for lay rescuers to locate an Automated External Defibrillator (AED) during sudden cardiac arrest events. To achieve this objective, the robot is equipped with an AED, and the Extended Kalman Filter is utilized for optimal indoor localization. The filter is implemented using data from the robot’s Inertial Measurement Unit, which comprises 9 Degrees of Freedom. The paper provides an explicit description of the performance of the Extended Kalman Filter in estimating the position of Ambubot, and demonstrates that the proposed approach is effective in accurately determining and estimating the robot’s position in unknown indoor environments. The results suggest that the proposed method is a promising solution for improving survival rates in cardiac arrest cases, and may have potential applications in other fields where accurate indoor localization is required.

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Distributed Robust Kalman Filters under Model Uncertainty and Multiplicative Disturbance

Cited by 6 publications

References 51 publications

Distributed Kalman filtering with event-triggered communication: a robust approach

Distributed Kalman filtering with event-triggered communication: a robust approach

Robust Kalman filters under epistemic uncertainty for non‐Gaussian systems with multiplicative noise

Implementation of extended kalman filter for localization of ambulance robot

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