التفاصيل البيبلوغرافية
| العنوان: |
Efficient Point Mass Predictor for Continuous and Discrete Models with Linear Dynamics |
| المؤلفون: |
Matousek, Jakub, Dunik, Jindrich, Brandner, Marek, Park, Chan Gook, Choe, Yeongkwon |
| سنة النشر: |
2023 |
| المجموعة: |
ArXiv.org (Cornell University Library) |
| 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: |
text |
| اللغة: |
unknown |
| العلاقة: |
http://arxiv.org/abs/2302.13827Test |
| الإتاحة: |
http://arxiv.org/abs/2302.13827Test |
| رقم الانضمام: |
edsbas.F4C200D1 |
| قاعدة البيانات: |
BASE |
