Stochastic event-triggered particle filtering for state estimation
المؤلف | Sadeghzadeh-Nokhodberiz, Nargess |
المؤلف | Davoodi, Mohammadreza |
المؤلف | Meskin, Nader |
تاريخ الإتاحة | 2021-06-07T09:59:12Z |
تاريخ النشر | 2016 |
اسم المنشور | 2016 2nd International Conference on Event-Based Control, Communication, and Signal Processing, EBCCSP 2016 - Proceedings |
المصدر | Scopus |
الملخص | In this paper, the problem of event-triggered (ET) state estimation is studied for nonlinear non-Gaussian systems. Particle filtering (PF) state estimation approach is developed for systems with stochastic ET measurements to overcome the computational problem in minimum mean square error (MMSE) estimators in which the posterior probability function is non-Gaussian due to ET measurement information. The proposed event triggered particle filtering (ETPF) not only solves the problem of non-Gaussianity but also can handle any functional nonlinearity in the system. It is proved that particles are weighted by the predicted event-triggering (ET) probability density function in the estimator side. The application of the proposed methodology to an interconnected four-tank system is also provided to illustrate and demonstrate the effectiveness of our proposed design methodology. |
اللغة | en |
الناشر | Institute of Electrical and Electronics Engineers Inc. |
الموضوع | Gaussian noise (electronic) Mean square error Monte Carlo methods Probability density function Signal filtering and prediction Signal processing State estimation Stochastic systems Computational problem Estimation approaches Event-triggering Measurement information Minimum mean-square error estimators Non-linear non-Gaussian Particle Filtering Posterior probability Information filtering |
النوع | Conference Paper |
الملفات في هذه التسجيلة
الملفات | الحجم | الصيغة | العرض |
---|---|---|---|
لا توجد ملفات لها صلة بهذه التسجيلة. |
هذه التسجيلة تظهر في المجموعات التالية
-
الهندسة الكهربائية [2579 items ]