Skip to main content
SpringerLink
Log in

Waveform design and high-resolution imaging of cognitive radar based on compressive sensing

  • Research Paper
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

We introduce the compressive sensing (CS) theory for waveform design of cognitive radar, and then propose an algorithm for the high-resolution radar signal waveform and its corresponding imaging method based on the sparse orthogonal frequency division multiplexing-linear frequency modulation (OFDM-LFM) signal. We first present the principle of spectrum synthesis and high-resolution imaging based on OFDM-LFM signals. Then, we propose the spectrum-sparse waveform design criterion and the reconstruction algorithm for a high-resolution range profile (HRRP) based on CS. Based on this, we analyze in detail the relationship between the scattering characteristics of the target and the parameters of the designed signal, and we construct the feedback of the target characteristics on the waveforms. Therefore, the “cognitive” function of radar can be achieved by adaptively adjusting the waveform with the target characteristics. Simulations are given to validate the effectiveness of the proposed algorithm.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Haykin S. Cognitive radar: a way of the future. IEEE Signal Proc Mag, 2006, 23: 30–40

    Article  Google Scholar 

  2. Goodman N A. Closed-loop radar with adaptively matched waveforms. In: 2007 International Conference on Electromagnetics in Advanced Applications. Torino: IEEE Press, 2007. 468–471

    Chapter  Google Scholar 

  3. Goodman N A, Venkata P R, Neifeld M A. Adaptive waveform design and sequential hypothesis testing for target recognition with active sensors. IEEE J Sel Top Signal Proces, 2007, 1: 105–113

    Article  Google Scholar 

  4. Wei Y, Meng H, Wang X. Adaptive single-tone waveform design for target recognition in cognitive radar. In: IET International Radar Conference. Guilin: IEEE Press, 2009. 1–4

    Google Scholar 

  5. Liu F, Wang J. An optimal ADP algorithm for waveform selection in cognitive radar systems. In: PIERS Proceedings. Beijing: Electromagnetics Academy Press, 2009. 728–730

    Google Scholar 

  6. Scala B L, Rezaeian M, Moran B. Optimal adaptive waveform selection for target tracking. In: 8th International Conference on Information Fusion. Philadelphia: IEEE Press, 2005. 25–28

    Google Scholar 

  7. Bell M R. Information theory and radar waveform design. IEEE Trans Inform Theory, 1993, 39: 1578–1579

    Article  MATH  Google Scholar 

  8. Donoho D L. Compressed sensing. IEEE Trans Inform Theory, 2006, 52: 1289–1306

    Article  MathSciNet  Google Scholar 

  9. Shi G M, Liu G H, Gao D H, et al. Advances in theory and application of compressed sensing (in Chinese). Acta Electron Sin, 2009, 37: 1070–1081

    Google Scholar 

  10. Shi G, Lin J, Chen X, et al. UWB echo signal detection with ultra-low rate sampling based on compressed sensing. IEEE Trans Circuits-II, 2008, 55: 379–383

    Google Scholar 

  11. Baraniuk R, Steeghs P. Compressive radar imaging. In: IEEE Radar Conference. Boston: IEEE Press, 2007. 128–133

    Chapter  Google Scholar 

  12. Yao Y, Petropulu A P, Poor H V. MIMO radar using compressive sampling. IEEE J Sel Top Signal Proces, 2010, 4: 146–163

    Article  Google Scholar 

  13. Xie X C, Zhang Y H. 2D radar imaging scheme based on compressive sensing technique (in Chinese). J Electron Inform Technol, 2010, 32: 1234–1238

    MathSciNet  Google Scholar 

  14. Quan Y H, Zhang L, Guo R, et al. Generating dense and super-resolution ISAR image by combining bandwidth extrapolation and compressive sensing. Sci China Inf Sci, 2011, 54: 2158–2169

    Article  MathSciNet  Google Scholar 

  15. Gao T, Zhou F, Li W, et al. A 6.2–9.5 GHz receiver for Wimedia MB-OFDM and China UWB standard. Sci China Inf Sci, 2011, 54: 407–418

    Article  Google Scholar 

  16. Zhang H, Dai X H, Pan D R. Linearly time-varying channel estimation and training power allocation for OFDM/MIMO systems using superimposed training. Sci China Inf Sci, 2011, 54: 1456–1470

    Article  Google Scholar 

  17. Shu F, BERBER S, Wang D M, et al. ML integer frequency offset estimation for OFDM systems with null subcarriers: Estimation range and pilot design. Sci China Inf Sci, 2010, 53: 2567–2575

    Article  Google Scholar 

  18. Chen Y F, Fan J H, Li W, et al. A current-mode RF transmitter for 6–9 GHz MB-OFDM UWB application. Sci China Inf Sci, 2011, 54: 419–428

    Article  Google Scholar 

  19. Prasad N N S S R K, Shameem V, Desai U B, et al. Improvement in target detection performance of pulse coded Doppler radar based on multicarrier modulation with fast Fourier transform (FFT). IEE P-Radar Son Nav, 2004, 151: 11–17

    Article  Google Scholar 

  20. Gu C, Zhang J D, Zhu X H. Signal processing and detecting for multicarrier modulated radar system based on OFDM (in Chinese). J Electron Inform Technol, 2009, 31: 1298–1300

    Google Scholar 

  21. Luo Y, Zhang Q, Qiu C W, et al. Micro-Doppler effect analysis and feature extraction in ISAR imaging with stepped-frequency chirp signals. IEEE Trans Geosci Remote, 2010, 48: 2087–2098

    Article  Google Scholar 

  22. Candès E J, Romberg J, Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete Fourier information. IEEE Trans Inform Theory, 2006, 52: 489–509

    Article  MathSciNet  MATH  Google Scholar 

  23. Candès E J, Tao T. Near-optimal signal recovery from random projections: universal encoding strategies. IEEE Trans Inform Theory, 2006, 52: 5406–5425

    Article  MathSciNet  Google Scholar 

  24. Baraniuk R. A lecture on compressive sensing. IEEE Signal Proc Mag, 2007, 24: 118–121

    Article  Google Scholar 

  25. Tropp J A, Gilbert A C. Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans Inform Theory, 2007, 53: 4655–4666

    Article  MathSciNet  Google Scholar 

  26. Kunis S, Rauhut H. Random sampling of sparse trigonometric polynomials II: orthogonal matching pursuit versus basis pursuit. Found Comput Math, 2007, 8: 737–763

    Article  MathSciNet  Google Scholar 

  27. Xin M, Bao Z. Properties of range profile of aircraft (in Chinese). Syst Eng Electron, 2002, 24: 65–70

    Google Scholar 

  28. Du L, Liu H W, Bao Z, et al. Radar automatic target recognition based on feature extraction for complex HRRP. Sci China Ser F-Inf Sci, 2008, 51: 1138–1153

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Telecommunication Engineering Institute, Air Force Engineering University, Xi’an, 710077, China

    Ying Luo & Qun Zhang

  2. Institute of Electronics, Chinese Academy of Sciences, Beijing, 100190, China

    Wen Hong & YiRong Wu

Authors
  1. Ying Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wen Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. YiRong Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ying Luo.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Luo, Y., Zhang, Q., Hong, W. et al. Waveform design and high-resolution imaging of cognitive radar based on compressive sensing. Sci. China Inf. Sci. 55, 2590–2603 (2012). https://doi.org/10.1007/s11432-011-4527-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11432-011-4527-x

Keywords

Search

Navigation