Motion Estimation Algorithm Based on GA-Optimized High-Order Extended Kalman Filter
School of Automation, Guangdong University of Petrochemical Technology, Maoming 525000, China
| Abstract: | To address the limitations of the Extended Kalman Filter (EKF) in handling strongly nonlinear systems, such as low accuracy, significant errors in robotic motion estimation, weak anti-interference capabilities, and sub-optimal performance in vehicle motion estimation, this paper introduces a novel vehicle motion estimation method based on a Genetic Algorithm-Optimized Higher Order Extended Kalman Filter (GA-HEKF). The HEKF algorithm enhances traditional EKF by incorporating hidden variables to mitigate round-off errors and pseudo-linearizing the vehicle motion model. This approach establishes a linear relationship between system variables and hidden variables, transforming the system state model into a linear form and equivalently rewriting the observation model. As a result, the vehicle motion model is reformulated into a structure compatible with Kalman filtering, enabling more accurate state estimation. To further improve the performance of the HEKF algorithm, an improved genetic algorithm is employed to optimize the covariance matrices of system noise and observation noise during the high-order extended Kalman filtering process. This optimization enhances the precision of vehicle state parameter estimation by dynamically adjusting the noise characteristics to better reflect real-world conditions. Simulation experiments are conducted to validate the effectiveness of the proposed method. The results demonstrate that the GA-HEKF algorithm outperforms traditional EKF in terms of accuracy, robustness, and computational efficiency. Specifically, it exhibits superior performance in scenarios with high nonlinearity and noise interference, making it a promising solution for advanced vehicle motion estimation applications. The proposed method not only addresses the inherent limitations of EKF but also provides a framework for improving state estimation in other complex nonlinear systems. |
| Keywords: | Motion Estimation; Higher Order Extended Kalman Filter; Genetic Algorithm |
| DOI: | 10.57237/j.cst.2025.02.001 |
View:
Downloads:
Received: 8 January 2025
Accepted: 18 February 2025
Published Online: 17 April 2025
Funding
| 1. | Guangdong Province Sci-ence and Technology Innovation Strategic Special Funding under Grant 2023S003042 |
| 2. | Maoming City Science and Technology Plan Project under Grant 2021002 |
| 3. | Maoming City Science and Technology Plan Project under Grant 2024012 |
| 4. | The Talent Introduction Project for Guangdong University of Petrochemical Technology under Grant 2020rc32. |
References
| [1] | Konieczny K. Technologia SLAM (Simultaneous Localization and Mapping) [J]. PRZEGLĄD GEODEZYJNY, 2023, (6): 47-52. |
| [2] | Smith R. C., Cheeseman P. On the Representation and Estimation of Spatial Uncertainty [J]. The International Journal of Robotics Research, 1986, Vol. 5(4): 56-68. |
| [3] | Montemerlo M., Sebastian Thrun, et al. FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem [C]. Proceedings of AAAI-2002, 2002. |
| [4] | Zhao Y, Wang T, Qin W, et al. Im-proved Rao-blackwellised Particle Filter based on Randomly Weighted Particle Swarm Optimization [J]. Computers and Electrical En-gineering, 2018, 71: 477-484. |
| [5] | Daixian Z., Yinan M., Mingbo W., et al. LSO-FastSLAM: A New Algorithm to Improve the Accuracy of Localization and Mapping for Rescue Robots [J]. Sensors, 2022, Vol. 22(3): 16-22. |
| [6] | Bailey T., Durrant-Whyte H.. Simultaneous Localisation and Mapping (SLAM): Part II State of the Art [J]. 2006(3): 15-22. |
| [7] | Pfingsthorn M., Slamet B., Visser A.. A Scalable Hybrid Multi-Robot SLAM Method for Highly Detailed Maps [C]. Robocup: Robot Soccer World Cup XI, July, Atlanta, USA, 2008. 1-48. |
| [8] | Mullane J., B. N. Vo, M. D. Adams. Rao-Blackwellised PHD SLAM [C]. IEEE International Conference on Robotics & Automation, 2010: 5410-5416. |
| [9] | Fei Z., Zijing Z., Luxi Y.. A New PHD-SLAM Method based on Memory Attenuation Filter [J]. Measurement Science and Technology, 2021, Vol. 32(9): 102-108. |
| [10] | Deusch H., Reuter S., Dietmayer K.. The Labeled Multi-Bernoulli SLAM Filter [J]. IEEE Signal Processing Letter. 2015, Vol. 22(10): 89-99. |
| [11] | Zhou Z., Wang D., Xu B.. A Multi-innovation with Forgetting Factor based EKF-SLAM Method for Mobile Robots [J]. Assembly Automation, 2020, Vol. 41(1): 71-78. |
| [12] | Shyam R., Sameer B., Daegyun C., et al. EKF-SLAM for Quadcopter Using Differential Flatness-Based LQR Control [J]. Electronics, 2023, Vol. 12(5): 1113-1113. |
| [13] | Hamza M., Mohamed A., Mustapha R.. An Efficient End-to-end EKF-SLAM Architecture based on LiDAR, GNSS, and IMU Aata Sensor Fusion for Autonomous Ground Vehicles [J]. Multimedia Tools and Applications, 2023, Vol. 83(18): 56183-56206. |
| [14] | R. Bucy, R. Kalman and I. Selin, Comment on "The Kalman Filter and Nonlinear Estimates of Multivariate Normal Processes" [J], IEEE Transactions on Automatic Control, Vol. 10(1): 33-38. |
| [15] | Fariña B., Toledo J., Acosta L.. Sensor Fusion Algorithm Selection for an Autonomous Wheelchair Based on EKF/UKF Comparison [J]. International Journal of Mechanical Engineering and Robotics Research, 2023, Vol. 12(1): 112-121. |
| [16] | Wen C. L., Cheng X. S., Xu D. X., et al. Filter design based on characteristic functions for one class of multi-dimensional nonlinear non-Gaussian systems [J]. Automatica, 2017(82): 171-180. |
| [17] | Jagan, B. O. L., Rao, S. K. Evaluation of DB-IEKF Algorithm Using Optimization Methods for Underwater Passive Target Tracking [J]. Mobile Networks and Applications, 2022, Vol. 27(3): 1-11. |
| [18] | J. Lin and F. Zhang. Loam livox: A fast, robust, high-precision LiDAR odometry and mapping package for LiDARs of small FoV [C]. 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, 2020, pp. 3126-3131. |
Join the Editorial Board
We invite active, qualified and high profile scientists and researchers to join as Editorial Board Members.
Join UsBecome a Reviewer
Scholars with a strong interest in reviewing are invited to join the reviewer panel to ensure the quality of the research to be published.
Join Us