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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.05482 |
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| _version_ | 1866914025789456384 |
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| author | Nooshabadi, Mohammad Hussein Yoosefian Lessard, Laurent |
| author_facet | Nooshabadi, Mohammad Hussein Yoosefian Lessard, Laurent |
| contents | We propose a new recursive estimator for linear dynamical systems under Gaussian process noise and non-Gaussian measurement noise. Specifically, we develop an approximate maximum a posteriori (MAP) estimator using dynamic programming and tools from convex analysis. Our approach does not rely on restrictive noise assumptions and employs a Bellman-like update instead of a Bayesian update. Our proposed estimator is computationally efficient, with only modest overhead compared to a standard Kalman filter. Simulations demonstrate that our estimator achieves lower root mean squared error (RMSE) than the Kalman filter and has comparable performance to state-of-the-art estimators, while requiring significantly less computational power. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_05482 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | State Estimation for Linear Systems with Non-Gaussian Measurement Noise via Dynamic Programming Nooshabadi, Mohammad Hussein Yoosefian Lessard, Laurent Systems and Control We propose a new recursive estimator for linear dynamical systems under Gaussian process noise and non-Gaussian measurement noise. Specifically, we develop an approximate maximum a posteriori (MAP) estimator using dynamic programming and tools from convex analysis. Our approach does not rely on restrictive noise assumptions and employs a Bellman-like update instead of a Bayesian update. Our proposed estimator is computationally efficient, with only modest overhead compared to a standard Kalman filter. Simulations demonstrate that our estimator achieves lower root mean squared error (RMSE) than the Kalman filter and has comparable performance to state-of-the-art estimators, while requiring significantly less computational power. |
| title | State Estimation for Linear Systems with Non-Gaussian Measurement Noise via Dynamic Programming |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2509.05482 |