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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/2511.02637 |
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| _version_ | 1866915596953714688 |
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| author | Behera, Priyank Kenley, C. Robert |
| author_facet | Behera, Priyank Kenley, C. Robert |
| contents | Multi-Target Tracking (MTT) is foundational for radar, defense, and autonomous systems, where tracking accuracy directly affects decision-making and safety. For linear systems with Gaussian process and measurement noise, the Kalman filter remains the gold standard for state estimation. However, its performance can degrade in real-world scenarios where measurement noise is temporally correlated. This violates the white-noise assumptions that Kalman filters have. Various approaches include state augmentation of the Kalman filter, but this approach is susceptible to failure due to ill-conditioned problem formulations. This work investigates the limitations of classical Kalman filtering in colored noise environments and presents an influence diagram-based approach to the Joint Probabilistic Data Association Filter (JPDAF). Simulation results on benchmark scenarios demonstrate that the Influence Diagram JPDAF (ID-JPDAF) achieves lower root mean square error (RMSE) than classical methods. These findings highlight the potential of influence diagram models for advancing multi-target tracking performance in radar and related applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_02637 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Influence Diagrams for Robust Multi-Target Tracking Behera, Priyank Kenley, C. Robert Optimization and Control Multi-Target Tracking (MTT) is foundational for radar, defense, and autonomous systems, where tracking accuracy directly affects decision-making and safety. For linear systems with Gaussian process and measurement noise, the Kalman filter remains the gold standard for state estimation. However, its performance can degrade in real-world scenarios where measurement noise is temporally correlated. This violates the white-noise assumptions that Kalman filters have. Various approaches include state augmentation of the Kalman filter, but this approach is susceptible to failure due to ill-conditioned problem formulations. This work investigates the limitations of classical Kalman filtering in colored noise environments and presents an influence diagram-based approach to the Joint Probabilistic Data Association Filter (JPDAF). Simulation results on benchmark scenarios demonstrate that the Influence Diagram JPDAF (ID-JPDAF) achieves lower root mean square error (RMSE) than classical methods. These findings highlight the potential of influence diagram models for advancing multi-target tracking performance in radar and related applications. |
| title | Influence Diagrams for Robust Multi-Target Tracking |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2511.02637 |