An Adaptive Cubature Kalman Filter for Target Tracking

Havangi, R.

doi:10.22061/jecei.2022.8168.486

Document Type : Original Research Paper

Author

R. Havangi

Faculty of Electrical Engineering and Computer ,University of Birjand, Birjand, Iran.

https://doi.org/10.22061/jecei.2022.8168.486

Abstract

Background and Objectives:The target tracking problem is an essential component of many engineering applications.The extended Kalman filter (EKF) is one of the most well-known suboptimal filter to solve target tracking. However, since EKF uses the first-order terms of the Taylor series nonlinear extension functions, it often makes large errors in the estimates of state. As a result, target tracking based on EKF may diverge.
Methods: In this manuscript, an adaptive square root cubature Kalman filter (ASRCKF) is poposed to solve the maneuvering target tracking problem. In the proposed method, the covariance of process and measurement noises is estimated adaptively. Thus, the performance of proposed method does not depend on the noise statistics and its performance is robust with unknown prior knowledge of the noise statistics. Morover, it has a consistently improved numerical stability why the matrices of covariance are guaranteed to remain semi- positive. The performance of the proposed method is compared with EKF, and the unscented Kalman filter (UKF) for target tracking problem.
Results:To evaluate the proposed method, many experiments is performed. The proposed method is evaluated on the non-maneuvering and maneuvering target tracking.
Conclusion: The results show that the proposed method has lower estimation errors with faster convergence rate than other methods. The proposed method can track the tates of moving target effectively and improve the accuracy of the system.

Keywords

Main Subjects

Object Tracking

Open Access

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit: http://creativecommons.org/licenses/by/4.0/

Publisher’s Note

JECEI Publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Publisher

Shahid Rajaee Teacher Training University

References

[1] M. Baradaran Khalkhali, A. Vahedian, H. Sadoghi Yazdi, “Multi-target state estimation using interactive Kalman filter for multi-vehicle tracking,” IEEE Trans. Intell. Transp. Syst., 21(3), 2020.

[2] M. Eltoukhy, M. Omair Ahmad, M.N.S. Swamy, “An adaptive turn rate estimation for tracking a maneuvering target,” IEEE Access, 8: 94176-94189, 2020.

[3] Z. Gong, G. Gao, M. Wang, “An adaptive particle filter for target tracking based on double space-resampling,” IEEE Access, 9: 91053-91061, 2021.

[4] X.R. Li, V.P. Jilkov, “Survey of maneuvering target tracking. Part II: Motion models of ballistic and space targets,” IEEE Trans. Aerosp. Electron. Syst. , 46(1): 96-110, 2010.

[5] S. Koteswara Rao, “Modified gain extended Kalman filter with application to bearings-only passive manoeuvring target tracking,” IEE Proc.-Radar Sonar Navig., 152(4): 239-244, August 2005 .

[6] L. Yang, L. Can ,L. Man, H. Xueyao ,W. Yanhua, “Cascaded Kalman filter for target tracking in automotive radar,” J. Eng., 2019(19), 2019.

[7] Y. Chen, W. Li , Y. Wang, “Online adaptive Kalman filter for target tracking with unknown noise statistics,” IEEE. Sens. Lett., 5(3), 2021.

[8] T.Ma, Q. Zhang, C. Chen, S. Gao, “Tracking of maneuvering star-convex extended target using modified adaptive extended Kalman filter,” IEEE Access, 4: 21430-21438, 2016.

[9] K. Doğançay, W.Y. Wang , N.H. Nguyen, “Bias-compensated diffusion pseudolinear Kalman filter algorithm for censored Bearings-only target tracking,” IEEE Signal Process. Lett., 26(11): 1703-1707, 2019.

[10] Y.Yang , X. Fan , Z. Zhuo , S. Wang , J. Nan , Y. Xu, “Amended Kalman filter for maneuvering target tracking,” Chinese J. Electron., 25(6): 1166-1171, 2016.

[11] J. Li, M. Ye, S. Jiao, W. Meng, X. Xu, “A novel state estimation approach based on adaptive unscented Kalman filter for electric vehicles,” IEEE Access, 8: 185629-185637, 2020.

[12] S.K. Raoa, K.R. Rajeswari, K. S.Lingamurty, “Unscented Kalman filter with application to bearings-only target tracking,” IETE J. Res., 55(2): 63-67, 2009.

[13] H. Zhang, G. Dai, J. Sun, Y. Zhao, “Unscented Kalman filter and its nonlinear application for tracking a moving target,” Optik, 124(20): 4468-4471, 2013.

[14] W. Zhoui, J. Hou, “A new adaptive robust unscented Kalman filter for improving the accuracy of target tracking,” IEEE Access, 7: 77476-77489, 2019.

[15] W. Zhou, J. Hou, “A new adaptive high-order unscented Kalman filter for improving the accuracy and robustness of target tracking,” IEEE Access, 7: 118484-118497, 2019.

[16] G. Yu. Kulikov, M.V. Kulikova, “Hyperbolic-SVD-Based square-root unscented Kalman filters in continuous-discrete target tracking scenarios,” IEEE Trans. Autom. Control, 67(1): 366-373, 2021.

[17] B. Ge, H. Zhang, L. Jiang, Z. Li, M. M. Butt, “Adaptive unscented Kalman filter for target tracking with unknown time-varying noise covariance,” sensors, 19(6): 1371, 2019.

[18] C. Liu, P. Shui, G. Wei, S. Li “Modified unscented Kalman filter using modified filter gain and variance scale factor for highly maneuvering target tracking,” J. Syst. Eng. Electron., 25(3): 380–385, 2014.

[19] I. Arasaratnam , S. Haykin, “Cubature Kalman filters,” IEEE Trans. Automat. Contr., 54(6):1254–1269, 2009.

[20] Q. Chen, C. Yin, J. Zhou, Y. Wang, X. Wang, C. Chen, ‘‘Hybrid consensus-based cubature Kalman filtering for distributed state estimation in sensor networks,’’ IEEE Sensors J., 18(11): 4561–4569, 2018.

[21] P.H. Leong, S. Arulampalam, T.A. Lamahewa, T.D. Abhayapala, “A Gaussian-sum based cubature Kalman filter for bearings-only tracking,” IEEE Trans. Aerosp. Electron. Syst., 49(2):1161–1176, 2013.

[22] H.W. Zhang, J.W. Xie, J.A. Ge, W.L. Lu, B.Z. Liu, “Strong tracking SCKF based on adaptive CS model for manoeuvring aircraft tracking,” IET Radar Sonar Navig., 12: 742–749, 2018.

[23] B. Gao, G. Hu, Y. Zhong, X. Zhu, “Cubature Kalman filter with both adaptability and robustness for tightly-coupled GNSS/INS integration,” IEEE Sensors J. , 21(13): 14997-15011, 2021.

[24] M. Yan, F. Fang, Y. Cai, “Maneuvering target tracking based on adaptive cooperative cubature Kalman filter,” in Proc. Chinese Automation Congress (CAC), 2019.

[25] A. Roy, D. Mitra, “Multi-target trackers using cubature Kalmanfilter for Doppler radar tracking in clutter”, IET Signal Process., 10(8): 888-901, 2016.

[26] B. Ristic, S. Arulampalam, N. Gordon, “Beyond the Kalman filter–particle filters for tracking applications,” Norwood, MA: Artech House, 2004.

[27] Y. Bar-Shalom, X.R. Li, T. Kirubarajan, Estimation with applications to tracking and navigation: theory algorithms and software. New York, John Wiley & Sons, 2004.

[28] M. Eltoukhy, M. Omir Ahmad, M.N.S. Swamy, “An adaptive turn rate estimation for tracking a maneuvering target,” IEEE Access, 8: 94176 – 94189, 2020.

[29] X.R. Li, V.P. Jilkov, ‘‘Survey of maneuvering target tracking. Part I. Dynamic models,’’ IEEE Trans. Aerosp. Electron. Syst., 39(4): 1333–1364, 2003.

[30] A. Zhang, S. Bao, F. Gao, W. Bi, “A novel strong tracking cubature Kalman filter and its application in maneuvering target tracking,” Chin. J. Aeronaut., 32(11): 2489–2502, 2019.

LETTERS TO EDITOR

Journal of Electrical and Computer Engineering Innovations (JECEI) welcomes letters to the editor for the post-publication discussions and corrections which allows debate post publication on its site, through the Letters to Editor. Letters pertaining to manuscript published in JECEI should be sent to the editorial office of JECEI within three months of either online publication or before printed publication, except for critiques of original research. Following points are to be considering before sending the letters (comments) to the editor.

[1] Letters that include statements of statistics, facts, research, or theories should include appropriate references, although more than three are discouraged.

[2] Letters that are personal attacks on an author rather than thoughtful criticism of the author’s ideas will not be considered for publication.

[3] Letters can be no more than 300 words in length.

[4] Letter writers should include a statement at the beginning of the letter stating that it is being submitted either for publication or not.

[5] Anonymous letters will not be considered.

[6] Letter writers must include their city and state of residence or work.

[7] Letters will be edited for clarity and length.

Name *

Email Address *

Affiliation *

Comments *

Security Code *

Journal of Electrical and Computer Engineering Innovations (JECEI)

An Adaptive Cubature Kalman Filter for Target Tracking

References

References

Send comment about this article

Volume 10, Issue 2
July 2022
Pages 425-436

An Adaptive Cubature Kalman Filter for Target Tracking

References

References

Send comment about this article

Volume 10, Issue 2July 2022Pages 425-436

Volume 10, Issue 2
July 2022
Pages 425-436