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Robust Trajectory Estimation for Maneuvering Targets Using an Adaptive Interacting Multiple Model Extended Kalman Filter | ||
| Journal of Modeling and Simulation in Electrical and Electronics Engineering | ||
| دوره 6، شماره 1 - شماره پیاپی 23، خرداد 2026، صفحه 53-65 اصل مقاله (893.68 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22075/mseee.2026.39451.1233 | ||
| نویسندگان | ||
| Alireza Jarrah؛ Hadi Asharioun* ؛ Mohammadhossein Hashemi | ||
| Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, Iran | ||
| تاریخ دریافت: 14 آبان 1404، تاریخ بازنگری: 01 بهمن 1404، تاریخ پذیرش: 18 بهمن 1404 | ||
| چکیده | ||
| Accurately tracking maneuvering targets remains a significant challenge in fields such as autonomous navigation and surveillance. This paper presents a robust solution using an Interacting Multiple Model Extended Kalman Filter (IMM-EKF). The proposed architecture adaptively combines three distinct kinematic models: a Near Constant Velocity (NCV) model for linear motion, a Coordinated Turn (CT) for constant turn rates, and a Coordinated Turn with Rate and Acceleration (CTRA) to handle aggressive maneuvers. The IMM framework dynamically weights each model's contribution based on the measurement likelihood, producing a fused state estimate that is more reliable than any single-model filter. The algorithm's performance was rigorously validated against ground truth data, demonstrating high precision with a position Root Mean Square Error (RMSE) of 0.3117 m and a yaw RMSE of 2.1614 degrees. Furthermore, the filter's statistical integrity was confirmed through consistency tests, with 94.16% of the Normalized Innovation Squared (NIS) values falling within the 95% confidence interval. These results underscore the effectiveness of the proposed multi-model approach for complex and dynamic trajectory estimation. | ||
| کلیدواژهها | ||
| State Estimation؛ Trajectory Estimation؛ Maneuvering Target Tracking؛ Extended Kalman Filter؛ Interacting Multiple Model؛ Motion Models | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 117 تعداد دریافت فایل اصل مقاله: 53 |
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