Skip to main content

A Feasible Approach for Weak Moving Target Detection Using Radar Echo

  • Conference paper
  • First Online:
Advances in Guidance, Navigation and Control

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 644))

  • 92 Accesses

Abstract

There are many difficulties in detecting weak targets from actually measured radar echo. In this paper, we present a feasible approach for weak moving target detection based on the mode decomposition algorithms. First, the EMD algorithm combined with the all-phase FFT was utilized to extract the features of weak moving targets for detection and classification. Considering the defects of EMD, the variational mode decomposition (VMD) algorithm substituted for EMD, which is a generation of the self-adaptive Wiener filter, to realize classification and detection of weak moving targets. The proposed method has good robustness to noise. Simultaneously, we gave the detection results of measured radar signals for experimental verification.

Sponsored by Natural Science Foundation of Shanghai (19ZR1454000).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 429.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 549.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 549.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Huang, N.E., Shen, Z., Long, S.R., et al.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proce. A 454(1971), 903–995 (1998)

    MathSciNet  MATH  Google Scholar 

  2. Oh, B.S., Guo, X., Wan, F.Y., et al.: Micro-Doppler mini-UAV classification using empirical-mode decomposition features. IEEE Geosci. Remote Sens. Lett. 15(2), 227–231 (2018)

    Article  Google Scholar 

  3. Weishaupt, F., Walterscheid, I., Biallawons, O., et al.: Vital Sign Localization and Measurement Using an LFMCW MIMO Radar. The19th International Radar Symposium (2018)

    Google Scholar 

  4. Nguyen, V., Weitnauer, A.M.A.: Denoised maximum likelihood estimation of chest wall displacement from the IR-UWB spectrum. IEEE Access 6, 15248–15258 (2018)

    Google Scholar 

  5. Zhao, G.L., Liang, Q.L., Durrani, T.S.: An EMD based Sense-Through-Foliage Target Detection UWB Radar Sensor Networks. IEEE. Access 6, 29254–29261 (2018)

    Article  Google Scholar 

  6. Chen, Z., Xie, F., Zhao, C., He, C.: Radio frequency interference mitigation for high-frequency surface wave radar. IEEE Geosci. Remote Sens. Lett. 15(7), 986–990 (2018)

    Article  Google Scholar 

  7. Xu, Q., Feng, Y., Jing, Z.: Underground compactness inversion algorithm based on hilbert marginal spectrum. 17th International Conference on Ground Penetrating Radar (GPR), pp. 1–4 (2018)

    Google Scholar 

  8. Zhang, X., Feng, X., Nilot, E., et al.: Noise suppression of GPR data using Variational Mode Decompostion. 17th International Conference on Ground Penetrating Radar (GPR) (2018)

    Google Scholar 

  9. Cexus, J.-C., Toumi, A.: Radar target recognition using time-frequency analysis and polar transformation.4th ATSIP (2018)

    Google Scholar 

  10. Yang, S.L., et al.: A gyroscope signal denoising method based on empirical mode decomposition and signal reconstruction. Sensors 19(23), 50–64 (2019)

    Google Scholar 

  11. Cui, B., Chen, X.: Improved hybrid filter for fiber optic gyroscope signal denoising based on EMD and forward linear prediction. Sens Actuators A Phys. 230, 150–155 (2015)

    Article  Google Scholar 

  12. Dragomiretskiy, K., Zosso, D.: Variational mode decomposition. IEEE Trans. Signal Process. 62(3), 531–544 (2014)

    Article  MathSciNet  Google Scholar 

  13. Bagheri, A., Ozbulut, O.E., Harris, D.K.: Structural system identification based on variational mode decomposition. J. Sound Vib. 417, 182–197 (2018)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Huiting Xia .

Editor information

Editors and Affiliations

Rights and permissions

Reprints 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

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Xia, H., Zhou, C., Yin, J., Gao, L., Liu, Y. (2022). A Feasible Approach for Weak Moving Target Detection Using Radar Echo. 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_63

Download citation

Publish with us

Policies and ethics