تقرير
Efficient Point Mass Predictor for Continuous and Discrete Models with Linear Dynamics
العنوان: | Efficient Point Mass Predictor for Continuous and Discrete Models with Linear Dynamics |
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المؤلفون: | Matousek, Jakub, Dunik, Jindrich, Brandner, Marek, Park, Chan Gook, Choe, Yeongkwon |
سنة النشر: | 2023 |
المجموعة: | Computer Science Mathematics Statistics |
مصطلحات موضوعية: | Electrical Engineering and Systems Science - Systems and Control, Electrical Engineering and Systems Science - Signal Processing, Mathematics - Statistics Theory |
الوصف: | This paper deals with state estimation of stochastic models with linear state dynamics, continuous or discrete in time. The emphasis is laid on a numerical solution to the state prediction by the time-update step of the grid-point-based point-mass filter (PMF), which is the most computationally demanding part of the PMF algorithm. A novel way of manipulating the grid, leading to the time-update in form of a convolution, is proposed. This reduces the PMF time complexity from quadratic to log-linear with respect to the number of grid points. Furthermore, the number of unique transition probability values is greatly reduced causing a significant reduction of the data storage needed. The proposed PMF prediction step is verified in a numerical study. Comment: Accepted for IFAC 2023 |
نوع الوثيقة: | Working Paper |
الوصول الحر: | http://arxiv.org/abs/2302.13827Test |
رقم الانضمام: | edsarx.2302.13827 |
قاعدة البيانات: | arXiv |
الوصف غير متاح. |