Skip to main content
Top
Published in:

12-06-2024

A feasibility study of microwave UAV imaging based on multi-station polarimetric radars

Authors: Haolin Zhang, Jiaxin Xie, Yabo Liu, Xin Zhao, Zhongjun Yu, Zicheng Wang, Shichao Chen

Published in: Journal of Computational Electronics | Issue 4/2024

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Effective regulation of unmanned aerial vehicles (UAVs) is crucial to social and public safety. In this paper, a microwave UAV imaging method is proposed for multi-station polarimetric radars. A polarimetric far-field scattering model is built to formulate the inverse scattering problem for various multi-station radar configurations. \(\mathscr {L}_1\)-norm regularization is incorporated in the inversion to realize a high spatial resolution. Numerical experiments are carried out with FEKO taking a typical quadcopter UAV as the target. Reconstruction results with polarization dependence of bistatic and multi-station radar configurations and multiple observation ranges are given. A spatial resolution study reveals the resolution of the proposed algorithm and analyzes the relationship between resolution and multiple factors. The results validate the feasibility of microwave UAV imaging with multi-station polarimetric radars.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literature
1.
go back to reference Roberge, V., Tarbouchi, M., Labonté, G.: Fast genetic algorithm path planner for fixed-wing military UAV using GPU. IEEE Trans. Aerosp. Electron. Syst. 54(5), 2105–2117 (2018)CrossRef Roberge, V., Tarbouchi, M., Labonté, G.: Fast genetic algorithm path planner for fixed-wing military UAV using GPU. IEEE Trans. Aerosp. Electron. Syst. 54(5), 2105–2117 (2018)CrossRef
2.
go back to reference Kim, J., Kim, S., Ju, C., et al.: Unmanned aerial vehicles in agriculture: a review of perspective of platform, control, and applications. IEEE Access 7, 105100–105115 (2019)CrossRef Kim, J., Kim, S., Ju, C., et al.: Unmanned aerial vehicles in agriculture: a review of perspective of platform, control, and applications. IEEE Access 7, 105100–105115 (2019)CrossRef
3.
go back to reference Shakhatreh, H., Sawalmeh, A.H., Al-Fuqaha, A., et al.: Unmanned aerial vehicles (UAVs): a survey on civil applications and key research challenges. IEEE Access 7, 48572–48634 (2019)CrossRef Shakhatreh, H., Sawalmeh, A.H., Al-Fuqaha, A., et al.: Unmanned aerial vehicles (UAVs): a survey on civil applications and key research challenges. IEEE Access 7, 48572–48634 (2019)CrossRef
4.
go back to reference Hayat, S., Yanmaz, E., Muzaffar, R.: Survey on unmanned aerial vehicle networks for civil applications: a communications viewpoint. IEEE Commun. Surv. Tutor. 18(4), 2624–2661 (2016)CrossRef Hayat, S., Yanmaz, E., Muzaffar, R.: Survey on unmanned aerial vehicle networks for civil applications: a communications viewpoint. IEEE Commun. Surv. Tutor. 18(4), 2624–2661 (2016)CrossRef
5.
go back to reference Menouar, H., Guvenc, I., Akkaya, K., et al.: UAV-enabled intelligent transportation systems for the smart city: applications and challenges. IEEE Commun. Mag. 55(3), 22–28 (2017)CrossRef Menouar, H., Guvenc, I., Akkaya, K., et al.: UAV-enabled intelligent transportation systems for the smart city: applications and challenges. IEEE Commun. Mag. 55(3), 22–28 (2017)CrossRef
6.
go back to reference Ezuma, M., Anjinappa, C.K., Semkin, V., et al.: Comparative analysis of radar-cross-section- based UAV recognition techniques. IEEE Sens. J. 22(18), 17932–17949 (2022)CrossRef Ezuma, M., Anjinappa, C.K., Semkin, V., et al.: Comparative analysis of radar-cross-section- based UAV recognition techniques. IEEE Sens. J. 22(18), 17932–17949 (2022)CrossRef
7.
go back to reference Guvenc, I., Koohifar, F., Singh, S., et al.: Detection, tracking, and interdiction for amateur drones. IEEE Commun. Mag. 56(4), 75–81 (2018)CrossRef Guvenc, I., Koohifar, F., Singh, S., et al.: Detection, tracking, and interdiction for amateur drones. IEEE Commun. Mag. 56(4), 75–81 (2018)CrossRef
8.
go back to reference Eaves, J.L., Reedy, E.K.: Principles of Modern Radar (1987) Eaves, J.L., Reedy, E.K.: Principles of Modern Radar (1987)
9.
go back to reference Röding, M., Sommerkorn, G., Häfner, S., et al.: Fully polarimetric wideband RCS measurements for small drones. In: 2017 11th European Conference on Antennas and Propagation (EUCAP), pp. 3926–3930 (2017) Röding, M., Sommerkorn, G., Häfner, S., et al.: Fully polarimetric wideband RCS measurements for small drones. In: 2017 11th European Conference on Antennas and Propagation (EUCAP), pp. 3926–3930 (2017)
10.
go back to reference Semkin, V., Haarla, J., Pairon, T., et al.: Analyzing radar cross section signatures of diverse drone models at mmwave frequencies. IEEE Access 8, 48958–48969 (2020)CrossRef Semkin, V., Haarla, J., Pairon, T., et al.: Analyzing radar cross section signatures of diverse drone models at mmwave frequencies. IEEE Access 8, 48958–48969 (2020)CrossRef
11.
go back to reference Rosamilia, M., Balleri, A., De Maio, A., et al.: Radar detection performance prediction using measured UAVs RCS data. IEEE Trans. Aerosp. Electron. Syst. 59(4), 3550–3565 (2023)CrossRef Rosamilia, M., Balleri, A., De Maio, A., et al.: Radar detection performance prediction using measured UAVs RCS data. IEEE Trans. Aerosp. Electron. Syst. 59(4), 3550–3565 (2023)CrossRef
12.
go back to reference Ezuma, M., Anjinappa, C.K., Funderburk, M., et al.: Radar cross section based statistical recognition of UAVs at microwave frequencies. IEEE Trans. Aerosp. Electron. Syst. 58(1), 27–46 (2022) Ezuma, M., Anjinappa, C.K., Funderburk, M., et al.: Radar cross section based statistical recognition of UAVs at microwave frequencies. IEEE Trans. Aerosp. Electron. Syst. 58(1), 27–46 (2022)
13.
go back to reference Fu, R., Al-Absi, M.A., Kim, K.H., et al.: Deep learning-based drone classification using radar cross section signatures at mmWave frequencies. IEEE Access 9, 161431–161444 (2021)CrossRef Fu, R., Al-Absi, M.A., Kim, K.H., et al.: Deep learning-based drone classification using radar cross section signatures at mmWave frequencies. IEEE Access 9, 161431–161444 (2021)CrossRef
14.
go back to reference Jian, M., Lu., Z., Chen, V.C.: Drone detection and tracking based on phase-interferometric doppler radar. In: 2018 IEEE Radar Conference (RadarConf18), pp. 1146–1149 (2018) Jian, M., Lu., Z., Chen, V.C.: Drone detection and tracking based on phase-interferometric doppler radar. In: 2018 IEEE Radar Conference (RadarConf18), pp. 1146–1149 (2018)
15.
go back to reference Gong, J., Yan, J., Li, D., et al.: Theoretical and experimental analysis of radar micro-doppler signature modulated by rotating blades of drones. IEEE Antennas Wirel. Propag. Lett. 19(10), 1659–1663 (2020)CrossRef Gong, J., Yan, J., Li, D., et al.: Theoretical and experimental analysis of radar micro-doppler signature modulated by rotating blades of drones. IEEE Antennas Wirel. Propag. Lett. 19(10), 1659–1663 (2020)CrossRef
16.
go back to reference Ritchie, M., Fioranelli, F., Griffiths, H., et al.: Monostatic and bistatic radar measurements of birds and micro-drone. In: 2016 IEEE Radar Conference (RadarConf), pp. 1–5 (2016) Ritchie, M., Fioranelli, F., Griffiths, H., et al.: Monostatic and bistatic radar measurements of birds and micro-drone. In: 2016 IEEE Radar Conference (RadarConf), pp. 1–5 (2016)
17.
go back to reference Kim, B.K., Kang, H.S., Park, S.O.: Drone classification using convolutional neural networks with merged doppler images. IEEE Geosci. Remote Sens. Lett. 14(1), 38–42 (2017)CrossRef Kim, B.K., Kang, H.S., Park, S.O.: Drone classification using convolutional neural networks with merged doppler images. IEEE Geosci. Remote Sens. Lett. 14(1), 38–42 (2017)CrossRef
18.
go back to reference Zhang, P., Yang, L., Chen, G., et al.: Classification of drones based on micro-doppler signatures with dual-band radar sensors. In: 2017 Progress in Electromagnetics Research Symposium—Fall (PIERS - FALL), pp. 638–643 (2017) Zhang, P., Yang, L., Chen, G., et al.: Classification of drones based on micro-doppler signatures with dual-band radar sensors. In: 2017 Progress in Electromagnetics Research Symposium—Fall (PIERS - FALL), pp. 638–643 (2017)
19.
go back to reference Kang, K.B., Choi, J.H., Cho, B.L., et al.: Analysis of micro-doppler signatures of small UAVs based on doppler spectrum. IEEE Trans. Aerosp. Electron. Syst. 57(5), 3252–3267 (2021)CrossRef Kang, K.B., Choi, J.H., Cho, B.L., et al.: Analysis of micro-doppler signatures of small UAVs based on doppler spectrum. IEEE Trans. Aerosp. Electron. Syst. 57(5), 3252–3267 (2021)CrossRef
20.
go back to reference Brooks, D.A., Schwander, O., Barbaresco, F., et al.: Temporal deep learning for drone micro-doppler classification. In: 2018 19th International Radar Symposium (IRS), pp. 1–10 (2018) Brooks, D.A., Schwander, O., Barbaresco, F., et al.: Temporal deep learning for drone micro-doppler classification. In: 2018 19th International Radar Symposium (IRS), pp. 1–10 (2018)
21.
go back to reference Kumawat, H.C., Chakraborty, M., Raj, A.A.B.: DIAT-RadSATNet-a novel lightweight DCNN architecture for micro-doppler-based small unmanned aerial vehicle (SUAV) targets’ detection and classification. IEEE Trans. Instrum. Meas. 71, 1–11 (2022) Kumawat, H.C., Chakraborty, M., Raj, A.A.B.: DIAT-RadSATNet-a novel lightweight DCNN architecture for micro-doppler-based small unmanned aerial vehicle (SUAV) targets’ detection and classification. IEEE Trans. Instrum. Meas. 71, 1–11 (2022)
22.
go back to reference Sun, Y., Fu, H., Abeywickrama, S., et al.: Drone classification and localization using micro-doppler signature with low-frequency signal. In: 2018 IEEE International Conference on Communication Systems (ICCS), pp. 413–417 (2018) Sun, Y., Fu, H., Abeywickrama, S., et al.: Drone classification and localization using micro-doppler signature with low-frequency signal. In: 2018 IEEE International Conference on Communication Systems (ICCS), pp. 413–417 (2018)
23.
go back to reference Björklund, S., Wadströmer, N.: Target detection and classification of small drones by deep learning on radar micro-doppler. In: 2019 International Radar Conference (RADAR), pp. 1–6 (2019) Björklund, S., Wadströmer, N.: Target detection and classification of small drones by deep learning on radar micro-doppler. In: 2019 International Radar Conference (RADAR), pp. 1–6 (2019)
24.
go back to reference Singh, A.K., Kim, Y.H.: Automatic measurement of blade length and rotation rate of drone using W-band micro-doppler radar. IEEE Sens. J. 18(5), 1895–1902 (2018)CrossRef Singh, A.K., Kim, Y.H.: Automatic measurement of blade length and rotation rate of drone using W-band micro-doppler radar. IEEE Sens. J. 18(5), 1895–1902 (2018)CrossRef
25.
go back to reference Li, T., Wen, B., Tian, Y., et al.: Numerical simulation and experimental analysis of small drone rotor blade polarimetry based on rcs and micro-doppler signature. IEEE Antennas Wirel. Propag. Lett. 18(1), 187–191 (2019)CrossRef Li, T., Wen, B., Tian, Y., et al.: Numerical simulation and experimental analysis of small drone rotor blade polarimetry based on rcs and micro-doppler signature. IEEE Antennas Wirel. Propag. Lett. 18(1), 187–191 (2019)CrossRef
26.
go back to reference Nie, W., Han, Z.C., Li, Y., et al.: UAV detection and localization based on multi-dimensional signal features. IEEE Sens. J. 22(6), 5150–5162 (2022)CrossRef Nie, W., Han, Z.C., Li, Y., et al.: UAV detection and localization based on multi-dimensional signal features. IEEE Sens. J. 22(6), 5150–5162 (2022)CrossRef
27.
go back to reference Torvik, B., Olsen, K.E., Griffiths, H.: Classification of birds and UAVs based on radar polarimetry. IEEE Geosci. Remote Sens. Lett. 13(9), 1305–1309 (2016)CrossRef Torvik, B., Olsen, K.E., Griffiths, H.: Classification of birds and UAVs based on radar polarimetry. IEEE Geosci. Remote Sens. Lett. 13(9), 1305–1309 (2016)CrossRef
28.
go back to reference Kim, B.K., Kang, H.S., Park, S.O.: Experimental analysis of small drone polarimetry based on micro-doppler signature. IEEE Geosci. Remote Sens. Lett. 14(10), 1670–1674 (2017)CrossRef Kim, B.K., Kang, H.S., Park, S.O.: Experimental analysis of small drone polarimetry based on micro-doppler signature. IEEE Geosci. Remote Sens. Lett. 14(10), 1670–1674 (2017)CrossRef
29.
go back to reference Kim, B.K., Kang, H.S., Lee, S., et al.: Improved drone classification using polarimetric merged-doppler images. IEEE Geosci. Remote Sens. Lett. 18(11), 1946–1950 (2021)CrossRef Kim, B.K., Kang, H.S., Lee, S., et al.: Improved drone classification using polarimetric merged-doppler images. IEEE Geosci. Remote Sens. Lett. 18(11), 1946–1950 (2021)CrossRef
30.
go back to reference Chew, W.C.: Waves and fields in inhomogeneous media (1995) Chew, W.C.: Waves and fields in inhomogeneous media (1995)
31.
go back to reference Donoho, D.L., Johnstone, I.M., Stern, H.A.S.: Maximum entropy and the nearly black object. J. R. Stat. Soc. 54(1), 41–81 (1992)MathSciNetCrossRef Donoho, D.L., Johnstone, I.M., Stern, H.A.S.: Maximum entropy and the nearly black object. J. R. Stat. Soc. 54(1), 41–81 (1992)MathSciNetCrossRef
32.
go back to reference Vogel, C., Oman, M.: Fast, robust total variation-based reconstruction of noisy, blurred images. IEEE Trans. Image Process. 7(6), 813–824 (1998)MathSciNetCrossRef Vogel, C., Oman, M.: Fast, robust total variation-based reconstruction of noisy, blurred images. IEEE Trans. Image Process. 7(6), 813–824 (1998)MathSciNetCrossRef
34.
go back to reference Golub, G., Loan, C.F.: Matrix Computations, 3rd edition (Johns Hopkins Studies in Mathematical Sciences) (1996) Golub, G., Loan, C.F.: Matrix Computations, 3rd edition (Johns Hopkins Studies in Mathematical Sciences) (1996)
35.
go back to reference Martorella, M., Palmer, J., Homer, J., et al.: On bistatic inverse synthetic aperture radar. IEEE Trans. Aerosp. Electron. Syst. 43(3), 1125–1134 (2007)CrossRef Martorella, M., Palmer, J., Homer, J., et al.: On bistatic inverse synthetic aperture radar. IEEE Trans. Aerosp. Electron. Syst. 43(3), 1125–1134 (2007)CrossRef
36.
go back to reference Kang, M.S., Lee, S.H., Kim, K.T., et al.: Bistatic ISAR imaging and scaling of highly maneuvering target with complex motion via compressive sensing. IEEE Trans. Aerosp. Electron. Syst. 54(6), 2809–2826 (2018)CrossRef Kang, M.S., Lee, S.H., Kim, K.T., et al.: Bistatic ISAR imaging and scaling of highly maneuvering target with complex motion via compressive sensing. IEEE Trans. Aerosp. Electron. Syst. 54(6), 2809–2826 (2018)CrossRef
Metadata
Title
A feasibility study of microwave UAV imaging based on multi-station polarimetric radars
Authors
Haolin Zhang
Jiaxin Xie
Yabo Liu
Xin Zhao
Zhongjun Yu
Zicheng Wang
Shichao Chen
Publication date
12-06-2024
Publisher
Springer US
Published in
Journal of Computational Electronics / Issue 4/2024
Print ISSN: 1569-8025
Electronic ISSN: 1572-8137
DOI
https://doi.org/10.1007/s10825-024-02185-2