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Influence factors of sparse microwave imaging radar system performance: approaches to waveform design and platform motion analysis

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Abstract

Sparse microwave imaging radar is a newly developed concept of microwave imaging system, which tries to combine the traditional radar imaging system with sparse signal processing theories, achieving the aim of reducing the complexity of microwave imaging systems and enhancing the system performance. In this paper, we introduce some basic concepts of sparse signal processing theory, and then apply it to the traditional radar imaging system to get the mathematical model of sparse microwave imaging system. We analyze the factors that determine the performance of sparse microwave imaging radar, including scene, waveform and platform. According to the radar model, we analyze how these factors influence the radar system and how to optimize them. Simulation results of the sparse microwave imaging radar system are also provided.

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References

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

    Article  MathSciNet  Google Scholar 

  2. Candès E J, Tao T. Near-optimal signal recovery from random projections: Universal encoding strategies. IEEE Tran Inf Theory, 2006, 52: 5406–5425

    Article  Google Scholar 

  3. Candès E J, Romberg J, Tao T. Stable signal recovery form incomplete and inaccurate measurements. Comm Pure Appl Math, 2006, 59: 1207–1223

    Article  MathSciNet  MATH  Google Scholar 

  4. Candès E J, Romberg J. Sparsity and incoherence in compressive sampling. Inver Prob, 2007, 23: 969–985

    Article  MATH  Google Scholar 

  5. Candès E J, Wakin M. An introduction to compressive sampling. IEEE Signal Process Mag, 2008, 25: 21–30

    Article  Google Scholar 

  6. Foucart S, Lai M J. Sparsest solutions of underdetermined linear systems via q-minimization for 0 < q ⩽ 1. Appl Comput Harmon Analys, 2009, 26: 395–407

    Article  MathSciNet  MATH  Google Scholar 

  7. Baraniuk R G, Steeghs P. Compressive radar imaging. In: Proceedings of IEEE Radar Conference, Boston, MA, 2007. 128–133

  8. Patel V M, Easley G R, Healy D M Jr, et al. Compressed synthetic aperture radar. IEEE J Sel Top Signal Process, 2010, 4: 244–254

    Article  Google Scholar 

  9. Ender J H G. On compressive sensing applied to radar. Signal Process, 2010, 90: 1402–1414

    Article  MATH  Google Scholar 

  10. Herman M A, Strohmer T. High-resolution radar via compressed sensing. IEEE Trans Signal Process, 2009, 57: 2275–2284

    Article  MathSciNet  Google Scholar 

  11. Alonso M T, López-Dekker P, Mallorquí J J. A novel strategy for radar imaging based on compressive sensing. IEEE Trans Geosc Remote Sens, 2010, 48: 4285–4295

    Article  Google Scholar 

  12. Berger C R, Zhou S, Willett P. Signal extraction using compressed sensing for passive radar with OFDM signals. In: Proceedings of 11th International Conference on Information Fusion, Colugue, 2008. 1–6

  13. Bahai A R S, Saltzberg B R. Multi-Carrier Digital Communications—Theory and Applications of OFDM. New York: Kluwer Academic/Plenum Publishers, 1999

    Google Scholar 

  14. Tropp J A, Laska J N, Duarte M F, et al. Beyond Nyquist: Efficient sampling of sparse bandlimited signals. IEEE Trans Inf Theory, 2010, 56: 520–544

    Article  MathSciNet  Google Scholar 

  15. Tropp J A, Wright S J. Computational methods for sparse solution of linear inverse problems. CalTech ACM Technical Report, No. 2009-1. 2009

  16. Bredies K, Lorenz D A. Linear convergence of iterative soft-thresholding. J Fourier Analys Appl, 2008, 14: 813–837

    Article  MathSciNet  MATH  Google Scholar 

  17. Daubechies I, Defriese M, De Mol C. An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Commun Pure Appl Math, 2004, 57: 1413–1457

    Article  MATH  Google Scholar 

  18. Candès E J, Tao T. Decoding by linear programming. IEEE Trans Inf Theory, 2005, 51: 4203–4215

    Article  Google Scholar 

  19. Ben-Haim Z, Eldar Y C, Elad M. Coherence-based performance guarantees for estimating a sparse vector under random noise. IEEE Trans Signal Process, 2010, 58: 5030–5043

    Article  MathSciNet  Google Scholar 

  20. Carrara W G, Goodman R S, Majewski R M. Spotlight Synthetic Aperture Radar: Signal Processing Algorithms. MA: Artech House Inc, 1995

    MATH  Google Scholar 

  21. Jakowatz C V Jr, Wahl D E, Eichel P H, et al. Spotlight-Mode Synthetic Aperture Radar: A Signal Processing Approach. MA: Kluwer Academic Publishers, 1996

    Book  Google Scholar 

  22. Cumming I G, Wong F H. Digital Processing of Synthetic Aperture Radar: Algorithms and Implementation. MA: Artech House Inc, 2005

    Google Scholar 

  23. Jiang H. Study on processing algorithm and analysis of imaging performance of compressed sensing radar via information theory model. Master Thesis. Beijing: Graduate University, Chinese Academy of Sciences. 2011

    Google Scholar 

  24. Jiang H, Zhang B C, Lin Y G, et al. Random noise SAR based on compressed sensing. In: Proceeding of IGARSS, Hawaii, 2010. 4624–4627

  25. Donoho D L, Tanner J. Precise undersampling theorems. Proceedings of the IEEE, 2010, 98: 913–924

    Article  Google Scholar 

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Correspondence to Zhe Zhang.

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Zhang, Z., Zhang, B., Jiang, C. et al. Influence factors of sparse microwave imaging radar system performance: approaches to waveform design and platform motion analysis. Sci. China Inf. Sci. 55, 2301–2317 (2012). https://doi.org/10.1007/s11432-012-4603-x

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  • DOI: https://doi.org/10.1007/s11432-012-4603-x

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