Abstract
Small unmanned aerial vehicles (UAVs) or micro drones are widely used for many applications in these years. But the misuse of micro drones may cause security issues. The problem of micro drone detection and parameters estimation is considered in this paper. Micro Doppler signatures of the drone’s rotating rotor blades are applied to identify micro drone. Firstly, the signal mathematical model of drone’s rotating multi-rotor blades is built and the flashes are used for detection. Then, the relationship between the micro Doppler signatures and the parameters of drones is analyzed. Furthermore, parameters, such as the number of blades, the number of rotor, the rotation rate and the length of blade, are estimated in this article. Finally, the results show that the effectiveness of the proposed method.
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
References
Wang, D., et al.: Weak target detection algorithm for non-cooperative bistatic radar. In: 2015 IET International Radar Conference, pp. 1–5 (2015)
Turhan, H.İ., Demirekler, M.: Detection of the weak targets by using multi-dimensional Hough transform based track before detect algorithm. In: 2016 IEEE Signal Processing and Communication Application Conference, pp. 1941–1944 (2016)
Rao, X., et al.: Detection of constant radial acceleration weak target via IAR-FRFT. IEEE Trans. Aerosp. Electron. Syst. 51(4), 3242–3253 (2015)
Fan, Y., et al.: Weak target detection in sea clutter background using local-multifractal spectrum with adaptive window length. IET Radar Sonar Navig. 9(7), 835–842 (2015)
Chen, V.C., et al.: Micro-Doppler effect in radar: phenomenon, model, and simulation study. IEEE Trans. Aerosp. Electron. Syst. 42(1), 2–21 (2006)
Kim, Y., Ling, H.: Human activity classification based on micro-Doppler signatures using a support vector machine. IEEE Trans. Geosci. Remote Sens. 47(5), 1328–1337 (2009)
Fioranelli, F., et al.: Performance analysis of centroid and SVD features for personnel recognition using multistatic micro-Doppler. IEEE Geosci. Remote Sens. Lett. 13(5), 725–729 (2016)
Tian, K., Li, J., Yang, X.: A novel method of micro-Doppler parameter extraction for human monitoring terahertz radar network. Ad Hoc Netw. 58, 222–230 (2017)
Chen, V.C.: Radar signatures of rotor blades. In: Aerospace/Defense Sensing, Simulation, and Controls, pp. 63–70. International Society for Optics and Photonics (2001)
Chen, R., Liu, B.: Extracting radar micro-Doppler signatures of helicopter rotating rotor blades using K-band radars. In: Defense and Security Symposium, pp. 908202–908202. International Society for Optics and Photonics (2014)
Acknowledgements
This work was supported by the National Natural Science Foundation of China (NSFC) under Grant U1433113, Sichuan province science and technology support plan 2015GZ0109, Shuangliu project of Research Institute in Chengdu, University of Electronic Science and Technology of China RW20140005 and in part by the Fundamental Research Funds for the Central Universities under Grants ZYGX2015J020.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Fang, X., Lu, C., Zhang, M., Min, R. (2019). Micro Drone Detection and Parameters Estimation Based on Micro-Doppler of Blades. In: Liang, Q., Mu, J., Jia, M., Wang, W., Feng, X., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2017. Lecture Notes in Electrical Engineering, vol 463. Springer, Singapore. https://doi.org/10.1007/978-981-10-6571-2_57
Download citation
DOI: https://doi.org/10.1007/978-981-10-6571-2_57
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-6570-5
Online ISBN: 978-981-10-6571-2
eBook Packages: EngineeringEngineering (R0)