Abstract
Conventional extended target tracking algorithms rely upon the hypothesis of the uniformly distributed scattering sources. However, the change of radar azimuth makes the statistics characteristic of the scattering source changed. In this paper, a robust star-convex extended target tracking algorithm is proposed to deal with this issue. Uniform distributed pseudo-measurement is generated by using the Centroid Contour Distance method (CCD) and Acceptance-Rejection sampling method, and then Double Layer Unscented Kalman filter (UKF) is derived to calculate the posterior intensity of kinematic state and shape parameters. Simulation results confirm the superiority of the proposed methods in comparison with classic UKF.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Challa, S., Morelande, M.R., Musicki, D., Evans, R.J.: Fundamentals of Object Tracking. Cambridge University Press, Cambridge (2013)
Sychev, M.I.: Precision tracking algorithms of civil aircraft by radar information. Russ. Aeronaut. 60(2), 190–197 (2017)
Chen, X., Li, Y., Li, Y.: Active sonar target tracking based on the GM-CPHD filter algorithm. **bei Gongye Daxue Xuebao J. Northwest. Polytechnical Univ. 36(4), 656–663 (2018)
Skolnik, M.: Radar Handbook, 3rd edn. Publishing House of Electronics Industry, Bei**g (2008)
Lan, J., Li, X.R.: Tracking of maneuvering non-ellipsoidal extended object or target group using random matrix. IEEE Trans. Signal Process. 62(9), 2450–2463 (2014)
Lan, J., Li, X.R.: Extended object or group target tracking using random matrix with nonlinear measurements. In: 19th International Conference on Information Fusion, pp. 1–8. IEEE Press, Heidelberg (2016)
Baum, M., Hanebeck, U.D.: Extended object tracking with random hypersurface models. IEEE Trans. Aerosp. Electron. Syst. 50(1), 149–159 (2014)
Aftab, W., Hostettler, R., De Freitas, D.A.: Spatio-temporal gaussian process models for extended and group object tracking with irregular shapes. J. IEEE Trans. Veh. Technol. 68(3), 2137–2151 (2019)
Hirscher, T., Scheel, A., Reuter, S.: Multiple extended object tracking using gaussian processes. In: 19th International Conference on Information Fusion, pp. 1–12. IEEE Press, Heidelberg (2016)
Baum, M., Noack, B., Hanebeck, U.D.: Random hypersurface mixture models for tracking multiple extended objects. In: 51th Conference on Decision and Control and European Control, pp. 1–6. IEEE Press (2012)
Zea, A., Faion, F., Baum, M.: Level-set random hypersurface models for tracking non-convex extended objects. In: 16th International Conference on Information Fusion, pp. 1–8. IEEE Press, Istanbul (2013)
Koch, J.: Bayesian approach to extended object and cluster tracking using random matrices. IEEE Trans. Aerosp. Electron. Syst. 44(3), 1042–1059 (2008)
Bordonaro, S., Willett, P., Bar-Shalom, Y.: Converted measurement sigma point kalman filter for bi-static sonar and radar tracking. J. IEEE Trans. Aerosp. Electr. Syst. (99), 1–12 (2018)
Wang, N., Ma, L., Xu, B.: Extended target probability hypothesis density filter based on cubature Kalman filter. IET Radar Sonar Navig. 9(3), 324–332 (2015)
Wyffels, K., Campbell, M.: Precision tracking via joint detailed shape estimation of arbitrary extended objects. IEEE Trans. Rob. 33(2), 313–332 (2017)
Özkan, E., Wahlstrom, N., Godsill, S.J.: Rao-blackwellised particle filter for star-convexextended target tracking models. In: 19th International Conference on Information Fusion, pp. 1–7. IEEE Press, Heidelberg (2016)
Freitas, D.A., Mihaylova, L., Amadou, G.: A box particle filter method for tracking multiple extended objects. IEEE Trans. Aerosp. Electron. Syst. 55(4), 1–13 (2016)
Granstrom, K., Baum, M.: Extended object tracking: introduction, overview and applications. J. Adv. Infor. Fusion 12(2), 1–30 (2017)
Ertl, T.: Computer Graphics — Principles and Practice. Springer, Vienna (1996)
Walker, R.J., Snoeyink, J.: Practical point-in-polygon tests using CSG representations of polygons. In: The 1999 International Conference on algorithm engineering and experimentation, pp. 114–123
Yang, S., Baum, M.: Second-order extended kalman filter for extended object and group tracking. In: 19th International Conference on Information Fusion, pp. 1–11. IEEE Press, Heidelberg (2016)
Sun, L., Zhang, S., Ji, B.: Performance evaluation for shape estimation of extended objects using a modified hausdorff distance. In: The 2016 IEEE International Conference on Information and Automation, pp. 780–784. IEEE Press, Ningbo (2016)
Acknowledgements
The work was supported in part by Education Department Research Project of Shaan xi Provincial Government (17JK0369), and the Industrial Science and Technology Research Project of Shaan xi Province (2019GY-069).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ma, T., Li, L., Chen, C., Kun, W., Li, Y. (2022). A Robust Unscented Kalman Filter for Tracking Star-Convex Extended Target with the Non-uniform Scattering Sources. In: Yan, L., Duan, H., Yu, X. (eds) Advances in Guidance, Navigation and Control . Lecture Notes in Electrical Engineering, vol 644. Springer, Singapore. https://doi.org/10.1007/978-981-15-8155-7_4
Download citation
DOI: https://doi.org/10.1007/978-981-15-8155-7_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-8154-0
Online ISBN: 978-981-15-8155-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)