Jarrah, Alireza, Asharioun, Hadi, Hashemi, Mohammadhossein. (1404). Robust Trajectory Estimation for Maneuvering Targets Using an Adaptive Interacting Multiple Model Extended Kalman Filter. هنرهای کاربردی, 6(1), 53-65. doi: 10.22075/mseee.2026.39451.1233
Alireza Jarrah; Hadi Asharioun; Mohammadhossein Hashemi. "Robust Trajectory Estimation for Maneuvering Targets Using an Adaptive Interacting Multiple Model Extended Kalman Filter". هنرهای کاربردی, 6, 1, 1404, 53-65. doi: 10.22075/mseee.2026.39451.1233
Jarrah, Alireza, Asharioun, Hadi, Hashemi, Mohammadhossein. (1404). 'Robust Trajectory Estimation for Maneuvering Targets Using an Adaptive Interacting Multiple Model Extended Kalman Filter', هنرهای کاربردی, 6(1), pp. 53-65. doi: 10.22075/mseee.2026.39451.1233
Jarrah, Alireza, Asharioun, Hadi, Hashemi, Mohammadhossein. Robust Trajectory Estimation for Maneuvering Targets Using an Adaptive Interacting Multiple Model Extended Kalman Filter. هنرهای کاربردی, 1404; 6(1): 53-65. doi: 10.22075/mseee.2026.39451.1233

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، بهار 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
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
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