From Target Tracking to Targeting Track -- Part III: Stochastic Process Modeling and Online Learning
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866914243798892544 |
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| author | Li, Tiancheng Wang, Jingyuan Li, Guchong Gao, Dengwei |
| author_facet | Li, Tiancheng Wang, Jingyuan Li, Guchong Gao, Dengwei |
| contents | This is the third part of a series of studies that model the target trajectory, which describes the target state evolution over continuous time, as a sample path of a stochastic process (SP). By adopting a deterministic-stochastic decomposition framework, we decompose the learning of the trajectory SP into two sequential stages: the first fits the deterministic trend of the trajectory using a curve function of time, while the second estimates the residual stochastic component through parametric learning of either a Gaussian process (GP) or Student's-$t$ process (StP). This leads to a Markov-free data-driven tracking approach that produces the continuous-time trajectory with minimal prior knowledge of the target dynamics. Notably, our approach explicitly models both the temporal correlations of the state sequence and of measurement noises through the SP framework. It does not only take advantage of the smooth trend of the target but also makes use of the long-term temporal correlation of both the data noise and the model fitting error. Simulations in four maneuvering target tracking scenarios have demonstrated its effectiveness and superiority in comparison with existing approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_05799 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Target Tracking to Targeting Track -- Part III: Stochastic Process Modeling and Online Learning Li, Tiancheng Wang, Jingyuan Li, Guchong Gao, Dengwei Systems and Control Signal Processing Machine Learning This is the third part of a series of studies that model the target trajectory, which describes the target state evolution over continuous time, as a sample path of a stochastic process (SP). By adopting a deterministic-stochastic decomposition framework, we decompose the learning of the trajectory SP into two sequential stages: the first fits the deterministic trend of the trajectory using a curve function of time, while the second estimates the residual stochastic component through parametric learning of either a Gaussian process (GP) or Student's-$t$ process (StP). This leads to a Markov-free data-driven tracking approach that produces the continuous-time trajectory with minimal prior knowledge of the target dynamics. Notably, our approach explicitly models both the temporal correlations of the state sequence and of measurement noises through the SP framework. It does not only take advantage of the smooth trend of the target but also makes use of the long-term temporal correlation of both the data noise and the model fitting error. Simulations in four maneuvering target tracking scenarios have demonstrated its effectiveness and superiority in comparison with existing approaches. |
| title | From Target Tracking to Targeting Track -- Part III: Stochastic Process Modeling and Online Learning |
| topic | Systems and Control Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2503.05799 |