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An Integrated Framework for Nonlinear Kalman Filtering Using Smooth Variable Structure Techniques
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Keywords
Nonlinear Kalman Filter
Smooth Variable Structure Filter
State Estimation
Adaptive Filtering
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Abstract
Nonlinear state estimation remains a pivotal challenge in control systems and signal processing, particularly under uncertainty, noise, and dynamic environmental conditions. This research proposes an integrated framework that combines nonlinear Kalman filtering with smooth variable structure filter (SVSF) techniques to enhance estimation accuracy and robustness in complex systems. The framework leverages the adaptive characteristics of SVSF to mitigate nonlinearities and uncertainties while maintaining the optimality properties of Kalman filters. Analytical modeling, simulation studies, and comparative evaluation demonstrate significant improvements in convergence speed, noise resilience, and tracking performance. The proposed approach offers a versatile methodology for applications in autonomous vehicles, multi-target tracking, and intelligent transportation systems, with measurable enhancements over conventional nonlinear filtering strategies (Ahmed et al., 2016; Bar-Shalom et al., 1995).
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References
1. R. Ahmed, M. El Sayed, S. A. Gadsden, J. Tjong, and S. Habibi, “Artificial neural network training utilizing the smooth variable structure filter estimation strategy,” Neural Comput. Appl., vol. 27, no. 3, pp. 537–548, Apr. 2016, doi: 10.1007/s00521-015-1875-2.
2. R. Ahmed, M. El Sayed, S. A. Gadsden, J. Tjong, and S. Habibi, “Automotive internal-combustion-engine fault detection and classification using artificial neural network techniques,” IEEE Trans. Veh. Technol., vol. 64, no. 1, pp. 21–33, Jan. 2015, doi: 10.1109/TVT.2014.2317736.
3. M. AlShabi and A. Elnady, “Recursive smooth variable structure filter for estimation processes in direct power control scheme under balanced and unbalanced power grid,” IEEE Syst. J., vol. 14, no. 1, pp. 971–982, Mar. 2020, doi: 10.1109/JSYST.2019.2919792.
4. Y. Bar-Shalom, X. R. Li, and T. Kirubarajan, Estimation With Applications to Tracking and Navigation: Theory Algorithms and Software. New York, NY, USA : Wiley, 2001.
5. Y. Bar-Shalom and X. R. Li, Multitarget-Multisensor Tracking: Principles and Techniques. Storrs, CT, USA : YBS Publishing, 1995.
6. B. Hassibi and T. Kailath, “H ∞ adaptive filtering,” in Proc. Int. Conf. Acoust., Speech, Signal Process., vol. 2, Detroit, MI, USA, May 1995, pp. 949–952, doi: 10.1109/ICASSP.1995.480332.
7. M. S. Grewal and A. P. Andrews, Kalman filtering: Theory and Practice with MATLAB. Hoboken, NJ, USA : Wiley, 2008.
8. S. Habibi, “The smooth variable structure filter,” Proc. IEEE, vol. 95, no. 5, pp. 1026–1059, May 2007, doi: 10.1109/JPROC.2007.893255.
9. I. Hwang, H. Balakrishnan, K. Roy, and C. Tomlin, “Multiple-target tracking and identity management in clutter, with application to aircraft tracking,” in Proc. Amer. Control Conf., Boston, MA, USA, 2004, pp. 3422–3428, doi: 10.23919/ACC.2004.1384439.
10. Y. Ho and R. Lee, “A Bayesian approach to problems in stochastic estimation and control,” IEEE Trans. Autom. Control, vol. AC-9, no. 4, pp. 333–339, Oct. 1964, doi: 10.1109/TAC.1964.1105763.
11. S. J. Julier and J. K. Uhlmann, “New extension of the Kalman filter to nonlinear systems,” Proc. SPIE, vol. 3068, pp. 182–193 Jul. 1997, doi: 10.1117/12.280797.
12. B. Kim, K. Yi, H.-J. Yoo, H.-J. Chong, and B. Ko, “An IMM/EKF approach for enhanced multitarget state estimation for application to integrated risk management system,” IEEE Trans. Veh. Technol., vol. 64, no. 3, pp. 876–889, Mar. 2015, doi: 10.1109/TVT.2014.2329497.
13. R. E. Kalman, “A new approach to linear filtering and prediction problems,” J. Basic Eng., vol. 82, no. 1, pp. 35–45, Mar. 1960, doi: 10.1115/1.3662552.
14. D. Schulz, W. Burgard, D. Fox, and A. B. Cremers, “Tracking multiple moving objects with a mobile robot,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. CVPR, Kauai, HI, USA, Dec. 2001, pp. I-371–I-377, doi: 10.1109/CVPR.2001.990499.
15. S. H. Rezatofighi, S. Gould, B. T. Vo, B.-N. Vo, K. Mele, and R. Hartley, “Multi-target tracking with time-varying clutter rate and detection profile: Application to time-lapse cell microscopy sequences,” IEEE Trans. Med. Imag., vol. 34, no. 6, pp. 1336–1348, Jun. 2015, doi: 10.1109/TMI.2015.2390647.
16. S. Rahimifard, R. Ahmed, and S. Habibi, “Interacting multiple model strategy for electric vehicle batteries state of charge/health/power estimation,” IEEE Access, vol. 9, pp. 109875–109888, 2021, doi: 10.1109/ACCESS.2021.3102607.
17. P. Setoodeh, S. Habibi, and S. Haykin, Nonlinear Filters: Theory and Applications. Hoboken, NJ, USA : Wiley, 2022.
18. C. Chen, L. Fragonara, and A. Tsourdos, “3D car tracking using fused data in traffic scenes for autonomous vehicle,” in Proc. 5th Int. Conf. Vehicle Technol. Intell. Transp. Syst., vol. 1, Crete, Greece, Jan. 2019, pp. 312–318, doi: 10.5220/0007674203120318.
19. J. Zhang, W. Xiao, B. Coifman, and J. P. Mills, “Vehicle tracking and speed estimation from roadside LiDAR,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 13, pp. 5597–5608, 2020, doi: 10.1109/JSTARS.2020.3024921.
20. E. A. Wan and R. van der Merwe, “The unscented Kalman filter for nonlinear estimation,” in Proc. IEEE Adapt. Syst. Signal Process., Commun., Control Symp. (AS-SPCC), Lake Louise, AB, Canada, Oct. 2000, pp. 153–158, doi: 10.1109/ASSPCC.2000.882463.
21. L. Xie, Y. C. Soh, and C. E. de Souza, “Robust Kalman filtering for uncertain discrete-time systems,” IEEE Trans. Autom. Control, vol. 39, no. 6, pp. 1310–1314, Jun. 1994, doi: 10.1109/9.293203.
22. M. Attari, Z. Luo, and S. Habibi, “An SVSF-based generalized robust strategy for target tracking in clutter,” IEEE Trans. Intell. Transp. Syst., vol. 17, no. 5, pp. 1381–1392, May 2016, doi: 10.1109/TITS.2015.2504331.