Report
Differentiable Bootstrap Particle Filters for Regime-Switching Models
| العنوان: | Differentiable Bootstrap Particle Filters for Regime-Switching Models |
|---|---|
| المؤلفون: | Li, Wenhan, Chen, Xiongjie, Wang, Wenwu, Elvira, Víctor, Li, Yunpeng |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Signal Processing, Computer Science - Machine Learning |
| الوصف: | Differentiable particle filters are an emerging class of particle filtering methods that use neural networks to construct and learn parametric state-space models. In real-world applications, both the state dynamics and measurements can switch between a set of candidate models. For instance, in target tracking, vehicles can idle, move through traffic, or cruise on motorways, and measurements are collected in different geographical or weather conditions. This paper proposes a new differentiable particle filter for regime-switching state-space models. The method can learn a set of unknown candidate dynamic and measurement models and track the state posteriors. We evaluate the performance of the novel algorithm in relevant models, showing its great performance compared to other competitive algorithms. Comment: 5 pages (4 pages of technical content, with 1 page of references), 2 figures, accepted by 22nd IEEE Statistical Signal Processing (SSP) workshop, camera-ready version |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2302.10319 |
| رقم الانضمام: | edsarx.2302.10319 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |