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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.08411 |
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| _version_ | 1866911557200379904 |
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| author | Zhumekenov, Abylay Beskos, Alexandros Crisan, Dan Jasra, Ajay Kantas, Nikolas |
| author_facet | Zhumekenov, Abylay Beskos, Alexandros Crisan, Dan Jasra, Ajay Kantas, Nikolas |
| contents | We consider the discrete-time filtering problem in scenarios where the observation noise is low or degenerate. We focus on the case where the observation equation is a linear function of the state and the data involve additive noise. However, we place minimal assumptions on the hidden state process. For such a class of models we derive new particle filters (PFs) with the key property that their performance is robust to the size of the observation noise. As a consequence, the developed PFs are well-defined in the limiting case of degenerate observation noise. Indicatively, we prove (under assumptions) that the PF applied in this low noise setting inherits the properties of the PF used in the degenerate case. We extend our framework to the case where the hidden states are drawn from a diffusion process. In this scenario we develop new PFs which are robust to both low noise and fine levels of time discretization. We illustrate our algorithms numerically on several examples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_08411 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Particle Filtering for a Class of State-Space Models with Low and Degenerate Observational Noise Zhumekenov, Abylay Beskos, Alexandros Crisan, Dan Jasra, Ajay Kantas, Nikolas Computation We consider the discrete-time filtering problem in scenarios where the observation noise is low or degenerate. We focus on the case where the observation equation is a linear function of the state and the data involve additive noise. However, we place minimal assumptions on the hidden state process. For such a class of models we derive new particle filters (PFs) with the key property that their performance is robust to the size of the observation noise. As a consequence, the developed PFs are well-defined in the limiting case of degenerate observation noise. Indicatively, we prove (under assumptions) that the PF applied in this low noise setting inherits the properties of the PF used in the degenerate case. We extend our framework to the case where the hidden states are drawn from a diffusion process. In this scenario we develop new PFs which are robust to both low noise and fine levels of time discretization. We illustrate our algorithms numerically on several examples. |
| title | Particle Filtering for a Class of State-Space Models with Low and Degenerate Observational Noise |
| topic | Computation |
| url | https://arxiv.org/abs/2601.08411 |