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Improved FOCUSS method for reconstruction of cluster structured sparse signals in radar imaging

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Abstract

The high resolution imaging of distributed targets in millimeter-wave radar system is studied in this paper. We use Gaussian Mrakov random field (GMRF) to represent the clustering property of the targets. Our novel method, called Clustering FOCUSS, incorporate an additional cluster constraint into the process of the focal underdetermined system solver (FOCUSS) algorithm. Simulation results indicate that the novel algorithm has a higher imaging accuracy than the methods of Capon beamforming, the l 1 norm algorithm and the FOCUSS algorithm.

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References

  1. Lee M S, Katkovnik V, Kim Y H. System modeling and signal processing for a switch antenna array radar. IEEE Trans Signal Process, 2004, 52: 1513–1523

    Article  Google Scholar 

  2. Johnson D H, Dudgeon D E. Array Signal Processing: Concepts and Techniques. Englewood Cliffs: Prentice-Hall, 1993

    MATH  Google Scholar 

  3. Van Trees H L. Detection, Estimation and Modulation Theory, Part IV: Optimum Array Processing. New York: John Wiley & Sons, Inc, 2002

    Google Scholar 

  4. Capon J. High-resolution frequency-wavenumber spectrum analysis. Proc IEEE, 1969, 57: 1408–1418

    Article  Google Scholar 

  5. Wang P, Meng H D, Wei Y M. FMCW radar imaging with multi-channel antenna array via sparse recovery technique. In: 2010 International Conference on Electrical and Control Engineering (ICECE), Wuhan, 2010. 1018–1021

  6. Stojnic M, Parvaresh F, Hassibi B. On the reconstruction of block-sparse signals with an optimal number of measurements. IEEE Trans Signal Process, 2009, 57: 3075–3085

    Article  MathSciNet  Google Scholar 

  7. Baraniuk R G, Cevher V, Duarte M F, et al. Model-based compressive sensing. IEEE Trans Inf Theory, 2010, 56: 1982–2001

    Article  MathSciNet  Google Scholar 

  8. Eldar Y C, Mishali M. Robust recovery of signals from a structured union of subspaces. IEEE Trans Inf Theory, 2009, 55: 5302–5316

    Article  MathSciNet  Google Scholar 

  9. Jenatton R, Obozinski G, Bach F. Structured sparse principal component analysis. In: Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS), Sardinia: 2010

  10. Kavukcuoglu K, Ranzato M A, Fergus R, et al. Learning invariant features through topographic filter maps. IEEE Conference on Computer Vision Pattern Recognition, Miami, 2009. 1605–1612

  11. Besag J. Spatial interaction and the statistical analysis of lattice systems (with discussion). J Royal Statist Soc Ser B, 1974, 36: 192–326

    MathSciNet  MATH  Google Scholar 

  12. Li S Z. Markov Random Field Modeling in Image Analysis. 2nd ed. New York: Springer-Verlag, 2011

    Google Scholar 

  13. Gorodnitsky I F, Rao B D. Sparse signal reconstruction from limited data using FOCUSS: a re-weighted minimum norm algorithm. IEEE Trans Signal Process, 1997, 45: 600–616

    Article  Google Scholar 

  14. Rao B D, Engan K, Cotter S F, et al. Subset selection in noise based on diversity measure minimization. IEEE Trans Signal Process, 2003, 51: 760–770

    Article  Google Scholar 

  15. Hu C X, Meng H D, Li G, et al. An improved FOCUSS algorithm for multi-channel FMCW radar imaging of distributed targets. In: Proceeding of 2011 IEEE CIE International Conference on Radar, Chengdu, 2011. 677–680

  16. Winkler V. Range Doppler detection for automotive FMCW radars. In: Microwave Conference, Munich, 2007. 1445–1448

  17. Potter L C, Ertin E, Parker J T, et al. Sparsity and Compressed Sensing in Radar Imaging. Proc IEEE, 2010, 98: 1006–1020

    Article  Google Scholar 

  18. Lin Y G, Wu Y R, Hong W, et al. Compressive sensing in radar imaging. In: 2009 IET International Radar Conference, Guilin, 2009. 1–3

  19. Candes E. The restricted isometry property and its implications for compressed sensing. C R Math, 2008, 346: 589–592

    MathSciNet  MATH  Google Scholar 

  20. Candes E J, Tao T. Near-optimal signal recovery from random projections: universal encoding strategies? IEEE Trans Inf Theory, 2006, 52: 5406–5425

    Article  MathSciNet  Google Scholar 

  21. Tello M, Lopez-Dekker P, Mallorqui J J. A novel strategy for radar imaging based on compressive sensing. IEEE Trans Geosci Remote Sens, 2010, 48: 4285–4295

    Article  Google Scholar 

  22. Lo Y T. A mathematical theory of antenna arrays with randomly spaced elements. IEEE Trans Antennas Propag, 1964, AP-12: 257–268

    Article  Google Scholar 

  23. Yang J Y, Peng Y G, Xu W L, et al. Ways to sparse representation: An overview. Sci China Ser F-Inf Sci, 2009, 52: 695–703

    Article  MathSciNet  MATH  Google Scholar 

  24. Andres M, Feil P, Menzel W, et al. Analysis of automobile scattering center locations by SAR measurements. In: 2011 IEEE Radar Conference (RADAR), Kansas, 2011. 109–112

  25. Stojanovic I, Cetin M, Karl W C. Joint space aspect reconstruction of wide-angle sar exploiting sparsity. In: SPIE Defense Security Symposium, Algorithms for Synthetic Aperture Radar Imagery XV, Orlando, 2008

  26. Chellappa R. Two-dimensional discrete gaussian Markov random field models for image processing. In: Kanal L N, Rosenfeld A, eds. Progress in Pattern Recognition 2. Amsterdam: Elsevier Science Publishing Co., 1984. 79–112

    Google Scholar 

  27. Wu Y, Wang X, Xiao P, et al. Fast algorithm based on triplet Markov fields for unsupervised multi-class segmentation of SAR images. Sci China Inf Sci, 2011, 54: 1524–1533

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  29. Derin H, Elliott H. Modeling and segmentation of noisy and textured images using Gibbs random fields. IEEE Trans Pattern Anal Mach Intell, 1987, PAMI-9: 39–55

    Article  Google Scholar 

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Correspondence to YiMin Liu.

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Hu, C., Liu, Y., Li, G. et al. Improved FOCUSS method for reconstruction of cluster structured sparse signals in radar imaging. Sci. China Inf. Sci. 55, 1776–1788 (2012). https://doi.org/10.1007/s11432-012-4628-1

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  • DOI: https://doi.org/10.1007/s11432-012-4628-1

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