Robust Trajectory Estimation for Maneuvering Targets Using an Adaptive Interacting Multiple Model Extended Kalman Filter

Document Type : Research Article

Authors

Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, Iran

Abstract

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.

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References

[1]        S. Zargari, A. Jarrah, and F. Baghbani, "Fuzzy-based driver monitoring system to assess dangerous driving on roads," Traffic Injury Prevention, pp. 1-10, 2025, doi: 10.1080/15389588.2025.2539923.
[2]        S. Zargari, A. Jarrah, and F. Baghbani, "Video-based Facial Expression Recognition Using DensNet121 and LSTM," in The First Conference on Artificial Intelligence and Intelligent Processing, 2022.
[3]        D.-J. Jwo, J.-H. Lai, and Y. Chang, "Interacting multiple model filter with a maximum correntropy criterion for gps navigation processing," Applied Sciences, vol. 13, no. 3, p. 1782, 2023, doi: 10.3390/app13031782.
[4]        S. A. Gadsden, S. R. Habibi, and T. Kirubarajan, "A novel interacting multiple model method for nonlinear target tracking," in 2010 13th International Conference on Information Fusion, 2010: IEEE, pp. 1-8, doi: 10.1109/ICIF.2010.5712021.
[5]        S. M. Aly, R. El Fouly, and H. Braka, "Extended Kalman filtering and interacting multiple model for tracking maneuvering targets in sensor networks," in 2009 Seventh Workshop on Intelligent solutions in Embedded Systems, 2009: IEEE, pp. 149-156.
[6]        W. Zhu, W. Wang, and G. Yuan, "An improved interacting multiple model filtering algorithm based on the cubature Kalman filter for maneuvering target tracking," Sensors, vol. 16, no. 6, p. 805, 2016, doi: 10.3390/s16060805.
[7]        L. Gao, J. Xing, Z. Ma, J. Sha, and X. Meng, "Improved IMM algorithm for nonlinear maneuvering target tracking," Procedia Engineering, vol. 29, pp. 4117-4123, 2012, doi: 10.1016/j.proeng.2012.01.630.
[8]        W. Li and Y. Jia, "Location of mobile station with maneuvers using an IMM-based cubature Kalman filter," IEEE Transactions on Industrial Electronics, vol. 59, no. 11, pp. 4338-4348, 2011, doi: 10.1109/TIE.2011.2180270.
[9]        M. Wan, P. Li, and T. Li, "Tracking maneuvering target with angle-only measurements using IMM algorithm based on CKF," in 2010 International Conference on Communications and Mobile Computing, 2010, vol. 3: IEEE, pp. 92-96, doi: 10.1109/CMC.2010.239.
[10]      K. Vedula, M. L. Weiss, R. C. Paffenroth, J. R. Uzarski, and D. R. Brown, "Maneuvering target tracking using the autoencoder-interacting multiple model filter," in 2020 54th Asilomar Conference on Signals, Systems, and Computers, 2020: IEEE, pp. 1512-1517, doi: 10.1109/IEEECONF51394.2020.9443396.
[11]      M. Ebrahimi, M. Ardeshiri, and S. A. Khanghah, "Bearing-only 2D maneuvering target tracking using smart interacting multiple model filter," Digital Signal Processing, vol. 126, p. 103497, 2022, doi: 10.1016/j.dsp.2022.103497.
[12]      X. Liu et al., "IMM-MOT: A Novel 3D Multi-object Tracking Framework with Interacting Multiple Model Filter," arXiv preprint arXiv:2502.09672, 2025, doi: 10.48550/arXiv.2502.09672.
[13]      J. Wang, X. Wang, Y. Chen, M. Yan, and H. Lan, "Model Adaptive Kalman Filter for Maneuvering Target Tracking Based on Variational Inference," Electronics, vol. 14, no. 10, p. 1908, 2025, doi: 10.3390/electronics14101908.
[14]      Y. Dong, W. Li, D. Li, C. Liu, and W. Xue, "Intelligent Tracking Method for Aerial Maneuvering Target Based on Unscented Kalman Filter," Remote Sensing, vol. 16, no. 17, p. 3301, 2024, doi: 10.3390/rs16173301.
[15]      J. Huang, J. Xie, H. Zhai, Z. Li, and W. Feng, "An Adaptive Constant Acceleration Model for Maneuvering Target Tracking," Remote Sensing, vol. 17, no. 5, p. 850, 2025, doi: 10.3390/rs17050850.
[16]      S. Wang, R. Li, C. Men, Y. Cao, and T.-S. Yeo, "Adaptive IMM Algorithm Based on Variational Inference for Multiple Maneuvering Extended Targets Tracking," Advances in Astronautics, pp. 1-15, 2025, doi: 10.1007/s42423-025-00173-7.
[17]      H. A. Blom and Y. Bar-Shalom, "The interacting multiple model algorithm for systems with Markovian switching coefficients," IEEE Transactions on Automatic Control, vol. 33, no. 8, pp. 780-783, 2002, doi: 10.1109/9.1299.
[18]      E. A. Wan and R. Van Der Merwe, "The unscented Kalman filter for nonlinear estimation," in Proceedings of the IEEE 2000 adaptive systems for signal processing, communications, and control symposium (Cat. No. 00EX373), 2000: IEEE, pp. 153-158, doi: 10.1109/ASSPCC.2000.882463.
[19]      J. J. LaViola, "A comparison of unscented and extended Kalman filtering for estimating quaternion motion," in Proceedings of the 2003 American Control Conference, 2003., 2003, vol. 3: IEEE, pp. 2435-2440, doi: 10.1109/ACC.2003.1243440.
[20]      A. Valade, P. Acco, P. Grabolosa, and J.-Y. Fourniols, "A study about Kalman filters applied to embedded sensors," Sensors, vol. 17, no. 12, p. 2810, 2017, doi: 10.3390/s17122810.
[21]      S. J. Julier and J. K. Uhlmann, "New extension of the Kalman filter to nonlinear systems," in Signal processing, sensor fusion, and target recognition VI, 1997, vol. 3068: Spie, pp. 182-193, doi: 10.1117/12.280797.
[22]         N. J. Gordon, D. J. Salmond, and A. F. Smith, "Novel approach to nonlinear/non-Gaussian Bayesian state estimation," in IEE proceedings F (radar and signal processing), 1993, vol. 140, no. 2: IET, pp. 107-113, doi: 10.1049/ip-f-2.1993.0015. 

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History

  • Receive Date: 05 November 2025
  • Revise Date: 21 January 2026
  • Accept Date: 07 February 2026

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