Hostname: page-component-7c8c6479df-27gpq Total loading time: 0 Render date: 2024-03-28T12:49:11.041Z Has data issue: false hasContentIssue false

Classification of UAV and bird target in low-altitude airspace with surveillance radar data

Published online by Cambridge University Press:  14 March 2019

W. S. Chen*
Affiliation:
Airport Research Institute, China Academy of Civil Aviation Science and Technology, Beijing, China
J. Liu
Affiliation:
Airport Research Institute, China Academy of Civil Aviation Science and Technology, Beijing, China
J. Li
Affiliation:
Airport Research Institute, China Academy of Civil Aviation Science and Technology, Beijing, China

Abstract

In order to ensure low-altitude safety, a tracking and recognition method of unmanned aerial vehicle (UAV) and bird targets based on traditional surveillance radar data is proposed. First, several motion models for UAV and flying bird targets are established. Second, the target trajectories are filtered and smoothed with multiple motion models. Third, by calculating the time-domain variance of the model occurrence probability, the model conversion probability of the target is estimated, and then the target type is identified and classified. The effectiveness and robustness of the algorithm is demonstrated by several groups of Monte Carlo simulation experiments, including setting different recognition steps, different model transformation probability, filtering and smoothing algorithm comparison. The algorithm is also successfully applied on the ground-truth radar data collected by the low-altitude surveillance radar at airport and coastal environments, where the targets of UAVs and flying birds could be tracked and recognised.

Type
Research Article
Copyright
© Royal Aeronautical Society 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Chen, W.S. The supervision, detection and jamming technologies for light and small UAV, China Civil Aviation, 2017, 253, (7), pp 3334.Google Scholar
2. Lv, X.M. Military UAV development and countermeasures, National Defense Sci and Technology, 2013, 34, (1), pp 57.Google Scholar
3. Chen, C.S. and Wang, S.Y. Infrared radiation characteristics measurement and temperature retrieval based on DJI unmanned aerial vehicle, Opto-Electronic Engineering, 2017, 44, (4), pp 427434.Google Scholar
4. Rohde and Schwarz . Automatic identification, positioning and suppression system of UAV (I), China Radio, 2016, 23, (8), pp 7273.Google Scholar
5. Rohde and Schwarz . Automatic identification, positioning and suppression system of UAV (II), China Radio, 2016, 23, (9), pp 7172.Google Scholar
6. Rohde and Schwarz . Automatic identification, positioning and suppression system of UAV (III), China Radio, 2016, 23, (10), pp 7273.Google Scholar
7. Lv, B., Wang, A.J., Ma, Y. and Zhou, Y.Z. Radio technology application in civil UAV control, China Radio, 2017, 8, pp 2426.Google Scholar
8. Chen, W.S., Yan, J. and Li, J. Joint optimization of detection and tracking with Rao-Blackwellized Monte Carlo data association, J Beijing University of Aeronautics and Astronautics, 2018, 44, (4), pp 700708.Google Scholar
9. Chen, X.L., Guan, J., Huang, Y. and He, Y. Radar low-observable target detection, Science & Technology Review, 2017, 35, (11), pp 3038.Google Scholar
10. Chen, X.L., Guan, J., Huang, Y., Yu, X.H., Liu, N.B., Dong, Y.L. and He, Y. Radar refined processing and its applications for low-observable moving target, Sci and Technology Review, 2017, 35, (20), pp 1927.Google Scholar
11. Zhang, J., Xu, Q.Y., Cao, X.B., Yan, P.K. and Li, X.L. Hierarchical incorporation of shape and shape dynamics for flying bird detection, Neurocomputing, 2014, 131, (5), pp 179190.Google Scholar
12. Bai, X.R., Xing, M.D., Zhou, F., Lu, G.Y. and Bao, Z. Imaging micromotion targets with rotating parts based on empirical-mode decomposition, IEEE Transactions on Geoscience and Remote Sensing, 2008, 46, (11), pp 35143523.Google Scholar
13. Stankovic, L., Thayaparan, T., Dakovic, M. and Popovic-Bugarin, V. Micro-Doppler removal in the radar imaging analysis, IEEE Transactions on Aerospace and Electronic Systems, 2013, 49, (2), pp 12341250.Google Scholar
14. Zhang, Q., Yeo, T.S., Tan, H.S. and Luo, Y. Imaging of a moving target with rotating parts based on the Hough transform, IEEE Transactions on Geoscience and Remote Sensing, 2008, 46, (1), pp 291299.Google Scholar
15. Zhang, Q., He, Q.F. and Luo, Y. Micro-Doppler feature extraction of group targets using signal decomposition based on Bessel function basis, J Electronics and Information Technology, 2016, 38, (12), pp 30563062.Google Scholar
16. Zhang, Q. and Luo, Y. Micro-Doppler Effect of Radar Targets, National Defense Industry Press, 2013, Beijing, PRC.Google Scholar
17. Chen, W.S. and Li, J. Radar target detection in low-altitude airspace with spatial features, Acta Aeronautica et Astronautica Sinica, 2015, 36, (9), pp 30603068.Google Scholar
18. Chen, W.S. Incoherent radar target detection and tracking with temporal features, Systems Engineering and Electronics, 2016, 38, (8), pp 18001807.Google Scholar
19. Chen, W.S. Spatial and temporal features selection for low-altitude target detection, Aerospace Sci and Technology, 2015, 40, (1), pp 171180.Google Scholar
20. Chen, W.S. and Li, J. Review on developments and applications of avian radar technology, Modern Radar, 2017, 39, (2), pp 717.Google Scholar