{"ID":2846259,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.02637","arxiv_id":"2511.02637","title":"Influence Diagrams for Robust Multi-Target Tracking","abstract":"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.","short_abstract":"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 degr...","url_abs":"https://arxiv.org/abs/2511.02637","url_pdf":"https://arxiv.org/pdf/2511.02637v1","authors":"[\"Priyank Behera\",\"C. Robert Kenley\"]","published":"2025-11-04T15:03:43Z","proceeding":"math.OC","tasks":"[\"math.OC\"]","methods":"[]","has_code":false}