Upper bound filter under interval constraints and multiplicative noises

Yong, Yang; Liang, Yan; Xu, Linfeng; Yang, Yanbo; Qin, Yuemei

doi:10.1049/iet-cta.2019.0253

Cited by 2 publications

(1 citation statement)

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“…The phenomenon of ROUs, if not dedicatedly tackled, would impair the global optimality of the state estimation problems, 19,20 thereby leading to undesirable deterioration of estimation performance. An alternative solution to the ROU‐induced problem is to achieve an acceptable bound (rather than the minimum bound) on the performance index and, in this regard, a few Gaussianity‐based state estimation strategies have been developed under the MMSE criterion, see, for example, References 21. Nevertheless, the corresponding MCKF problem has not been fully investigated yet for the non‐Gaussian systems with ROUs, and this constitutes the main motivation of our current investigation.…”

Section: Introductionmentioning

confidence: 99%

Maximum‐correntropy‐based Kalman filtering for time‐varying systems with randomly occurring uncertainties: An event‐triggered approach

Wang

Zou

et al. 2020

Intl J Robust & Nonlinear

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In this article, the maximum‐correntropy‐based Kalman filtering problem is investigated for a class of linear time‐varying systems in the presence of non‐Gaussian noises and randomly occurring uncertainties (ROUs). The random nature of the parameter uncertainties is characterized by a stochastic variable conforming to the Bernoulli distribution. In order to avoid unnecessary data transmission and reduce consumption of limited communication resource, the event‐triggered mechanism (ETM) is introduced in the sensor‐to‐filter channel to decide whether the data should be transmitted or not. A novel performance index is first proposed to reflect the joint effects from the non‐Gaussian noises, the ETM as well as the ROUs. Under the proposed performance index, an event‐based Kalman filter is then constructed whose gain is calculated based on the maximum correntropy criterion. Finally, the effectiveness of the proposed filtering scheme is verified via a practical target tracking example.

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Section: Introductionmentioning

confidence: 99%