Stochastic Integration Based Estimator: Robust Design and Stone Soup Implementation
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916565582086144 |
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| author | Dunik, Jindrich Matousek, Jakub Straka, Ondrej Blasch, Erik Hiles, John Niu, Ruixin |
| author_facet | Dunik, Jindrich Matousek, Jakub Straka, Ondrej Blasch, Erik Hiles, John Niu, Ruixin |
| contents | This paper deals with state estimation of nonlinear stochastic dynamic models. In particular, the stochastic integration rule, which provides asymptotically unbiased estimates of the moments of nonlinearly transformed Gaussian random variables, is reviewed together with the recently introduced stochastic integration filter (SIF). Using SIF, the respective multi-step prediction and smoothing algorithms are developed in full and efficient square-root form. The stochastic-integration-rule-based algorithms are implemented in Python (within the Stone Soup framework) and in MATLAB and are numerically evaluated and compared with the well-known unscented and extended Kalman filters using the Stone Soup defined tracking scenario. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_07239 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Stochastic Integration Based Estimator: Robust Design and Stone Soup Implementation Dunik, Jindrich Matousek, Jakub Straka, Ondrej Blasch, Erik Hiles, John Niu, Ruixin Signal Processing This paper deals with state estimation of nonlinear stochastic dynamic models. In particular, the stochastic integration rule, which provides asymptotically unbiased estimates of the moments of nonlinearly transformed Gaussian random variables, is reviewed together with the recently introduced stochastic integration filter (SIF). Using SIF, the respective multi-step prediction and smoothing algorithms are developed in full and efficient square-root form. The stochastic-integration-rule-based algorithms are implemented in Python (within the Stone Soup framework) and in MATLAB and are numerically evaluated and compared with the well-known unscented and extended Kalman filters using the Stone Soup defined tracking scenario. |
| title | Stochastic Integration Based Estimator: Robust Design and Stone Soup Implementation |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2412.07239 |