Advertisement

A Novel Range Super-Resolution Algorithm for UAV Swarm Target Based on LFMCW Radar

  • Tianyuan Yang
  • Tao SuEmail author
  • Jibin ZhengEmail author
Conference paper
  • 16 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 571)

Abstract

We consider the problem of estimating range about unmanned aerial vehicle (UAV) swarm target. The main challenges are small radar cross-section (RCS) and high target density. In this paper, for better accumulation, we focus the original beat signal by Focus-Before-Detect (FBD) and obtain the velocity. Based on the velocity, we form a compensation matrix to eliminate the range migration (RM). Then, a novel range super-resolution algorithm based on the gridless sparse method is implemented that improves the range resolution to a great extent. Experimental results based on simulated and real measured data are carried out to demonstrate the accuracy of the model and the effectiveness of the algorithm.

Keywords

Swarm target detection UAV target detection Gridless sparse methods Range super-resolution Reweighted atomic norm minimization (RAM) 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (grant 61601341 and 61771367), Project Funded by China Postdoctoral Science Foundation (grant 2015M582615 and 2016T90891), Program for the National Science Fund for Distinguished Young Scholars (grant 61525105), National Natural Science Foundation of Shaanxi Province, Key R&D Program–The Key Industry Innovation Chain of Shaanxi (grant 2018JM6060) and the 111 Project (B18039).

References

  1. 1.
    Perry R, Dipietro R, Fante R (1999) SAR imaging of moving targets. IEEE Trans Aerosp Electron Syst 35(1):188–200CrossRefGoogle Scholar
  2. 2.
    Zheng J, Su T, Zhu W, He X, Liu QH (2014) Radar high-speed target detection based on the scaled inverse fourier transform. IEEE J Sel Top Appl Earth Obs Remote Sens 8(3):1108–1119Google Scholar
  3. 3.
    Stoica P, Moses RL et al (2005) Spectral analysis of signals. Pearson Prentice Hall, Upper Saddle RiverGoogle Scholar
  4. 4.
    Malioutov D, Cetin M, Willsky AS (2005) A sparse signal reconstruction perspective for source localization with sensor arrays. IEEE Trans Signal Process 53(8):3010–3022MathSciNetCrossRefGoogle Scholar
  5. 5.
    Zhu H, Leus G, Giannakis GB (2011) Sparsity-cognizant total least-squares for perturbed compressive sampling. IEEE Trans Signal Process 59(5):2002–2016MathSciNetCrossRefGoogle Scholar
  6. 6.
    Fang J, Wang F, Shen Y, Li H, Blum RS (2016) Super-resolution compressed sensing for line spectral estimation: an iterative reweighted approach. IEEE Trans Signal Process 64(18):4649–4662MathSciNetCrossRefGoogle Scholar
  7. 7.
    Chandrasekaran V, Recht B, Parrilo PA, Willsky AS (2012) The convex geometry of linear inverse problems. Found Comput Math 12(6):805–849MathSciNetCrossRefGoogle Scholar
  8. 8.
    Candès EJ, Fernandez-Granda C (2014) Towards a mathematical theory of super-resolution. Commun Pure Appl Math 67(6):906–956MathSciNetCrossRefGoogle Scholar
  9. 9.
    Tang G, Bhaskar BN, Shah P, Recht B (2013) Compressed sensing off the grid. IEEE Trans Inf Theory 59(11):7465–7490MathSciNetCrossRefGoogle Scholar
  10. 10.
    Yang Z, Xie L (2016) Enhancing sparsity and resolution via reweighted atomic norm minimization. IEEE Trans Signal Process 64(4):995–1006MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.National Laboratory of Radar Signal ProcessingXidian UniversityXi’anChina

Personalised recommendations