Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Multi-channels wideband digital reconnaissance receiver based on compressed sensing

In order to meet the bandwidth need of modern wideband digital reconnaissance receiver, an implementation based on compressed sensing is proposed. The compressed sensing method is directly used to sample and reconstruct multi-band RF signal in this receiver. The original signal is mixed with Bernoulli random signal, and then filtered by low-pass filter. As completing the multiple narrow-band signals recovery within broadband range, the sampling signal is reconstructed in the digital domain. In this study, a novel reconstruction algorithm is proposed, which is adaptive conjugate gradient pursuit multiple measurement vectors (ACGPMMV), to overcome the drawback of orthogonal matching pursuit multiple measurement vectors (OMPMMV). Meanwhile, how to reduce the channel number of system required is also analysed, which will be reduce hardware costs. The shortcoming of limitation of sampling chip's analogue bandwidth in the parallel alternating sampling system is overcame, and the requirement of receiver bandwidth is achieved by using the lower rate sampling. In this study, the study is carried out by means of numerical simulations of multi-signals in the different system model. The conclusions illuminate the algorithm can get well-signal reconstruction results under the conditions of less channel and low sampling rate, and well-noise stability.

References

    1. 1)
      • 13. Kim, S.J., Koh, K., Lustig, M., et al: ‘A method for large-scale-regularized least- squares’, IEEE J. Sel. Top. Signal Process., 2007, 4, (1), pp. 606617 (doi: 10.1109/JSTSP.2007.910971).
    2. 2)
      • 10. Needell, D., Vershynin, R.: Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit. [accessed 28 June 2011]. Available at http://www.math.ucdavis.edu/%vershynin/papers/ROMP.pdf.
    3. 3)
      • 24. Jie, C., Xiaoming, H.: ‘Theoretical results on sparse representations of multiple-measurement vectors’, IEEE Trans. Signal Process., 2006, 54, (12), pp. 46344643 (doi: 10.1109/TSP.2006.881263).
    4. 4)
      • 9. Needell, D., Vershynin, R.: Signal recovery from incomplete and inaccurate measurements via regularized orthogonal matching pursuit. [accessed 28 June 2011]. Available at http://www.math.ucdavis.edu/%7Evershynin/papers/ROMP-stability.pdf.
    5. 5)
      • 7. Needell, D., Tropp, J.A.: ‘CoSaMP: iterative signal recovery from incomplete and inaccurate samples’, Appl. Comput. Harmon. Anal., 2009, 26, (3), pp. 301321 (doi: 10.1016/j.acha.2008.07.002).
    6. 6)
      • 19. Lun, M., Zhenfang, L., Guisheng, L.: ‘Approach to sample the broadband radar signal with multi- channel and low-rate ADC’, Syst. Eng. Electron., 2007, 29, (9), pp. 14531455.
    7. 7)
      • 22. Cotter, S.F., Rao, B.D., Engan, K., et al: ‘Sparse solutions to linear inverse problems with multiple measurement vectors’, IEEE Trans. Signal Process., 2005, 53, (7), pp. 24772488 (doi: 10.1109/TSP.2005.849172).
    8. 8)
      • 27. Blumensath, T., Davies, M.: ‘Gradient pursuits’, IEEE Trans. Signal Process., 2008, 56, (7), pp. 23702382 (doi: 10.1109/TSP.2007.916124).
    9. 9)
      • 20. Mishali, M., Yonina, C.E.: ‘Blind multiband signal reconstruction: compressed sensing for analog signals’, IEEE Trans. Signal Process., 2009, 57, (3), pp. 9931009 (doi: 10.1109/TSP.2009.2012791).
    10. 10)
      • 14. Figueiredo, M.A.T., Nowak, R.D., Wright, S.J.: ‘Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems’, IEEE J. Sel. Top. Signal Process.: Special Issue on Convex Optimization Methods for Signal Processing, 2007, 1, (4), pp. 586598 (doi: 10.1109/JSTSP.2007.910281).
    11. 11)
      • 21. Shi, G., Lin, J., Chen, X., Qi, F., Liu, D., Zhang, L.: ‘UWB echo signal detection with ultra-low rate sampling based on compressed sensing’, IEEE Trans. Circuits Syst. II, Express Briefs, 2008, 55, (4), pp. 379383 (doi: 10.1109/TCSII.2008.918988).
    12. 12)
      • 6. Candès, E.: ‘The restricted isometry property and its implications for compressed sensing’, Acad. Sci., 2006, 346, (I), pp. 598592.
    13. 13)
      • 25. Cotter, S.F., Rao, B.D.: ‘Sparse solution to linear inverse problems with multiple measurement vectors’, IEEE Trans. Signal Process., 2005, 53, (7), pp. 24772487 (doi: 10.1109/TSP.2005.849172).
    14. 14)
      • 3. Donoho, D.L.: ‘Compressed sensing’, IEEE Trans. Inf. Theory, 2006, 52, (4), pp. 12891306 (doi: 10.1109/TIT.2006.871582).
    15. 15)
      • 4. Donoho, D.L., Tsaig, Y.: ‘Extensions of compressed sensing’, Signal Process., 2006, 86, (3), pp. 533548 (doi: 10.1016/j.sigpro.2005.05.027).
    16. 16)
      • 26. Hyder, M.M., Mahata, K.: ‘A robust algorithm for joint-sparse recovery’, IEEE Signal Process. Lett., 2009, 16, (12), pp. 10911094 (doi: 10.1109/LSP.2009.2028107).
    17. 17)
      • 2. Candès, E., Romberg, J., Tao, T.: ‘Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information, IEEE Trans. Inf. Theory, 2006, 52, (2), pp. 489509 (doi: 10.1109/TIT.2005.862083).
    18. 18)
      • 12. Tropp, J.A., ‘Algorithms for simultaneous sparse approximation. Part II: Convex relaxation’, Signal Process., 2006, 86, pp. 589602 (doi: 10.1016/j.sigpro.2005.05.031).
    19. 19)
      • 23. Mishali, M., Yonina, C.E.: ‘Reduce and boost: recovering arbitrary sets of jointly sparse vectors’, IEEE Trans. Signal Process., 2008, 56, (10), pp. 46924702 (doi: 10.1109/TSP.2008.927802).
    20. 20)
      • 17. Wei, H.: ‘A new wide-band digital receiver-high-speed frequency estimation with short-length data’ (Cheng Du: University of Electronic Science and Technology of China, 2004).
    21. 21)
      • 11. Tropp, J.A.: ‘Algorithms for simultaneous sparse approximation. Part I: Greedy pursuit’, Signal Process., 2006, 86, pp. 572588 (doi: 10.1016/j.sigpro.2005.05.030).
    22. 22)
      • 1. Jianchao, G., Juhong, Z.: ‘Wide-band digital signal processing with application in electronic warfare’, Inf. Electron. Eng., 2010, 8, (4), pp. 425430.
    23. 23)
      • 5. Baraniuk, R.: ‘A lecture on compressive sensing’, IEEE Signal Process. Mag., 2007, 24, (4), pp. 118121 (doi: 10.1109/MSP.2007.4286571).
    24. 24)
      • 15. Daubechies, I., Defrise, M., De Mol, C.: ‘An iterative thresholding algorithm for linear inverse problems with a sparsity constraint’, Commun. Pure Appl. Math., 2004, 57, (11), pp. 14131457 (doi: 10.1002/cpa.20042).
    25. 25)
      • 8. Donoho, D.L., Tsaig, Y., Drori, I., Drori, I., Starck, J.L.: ‘Sparse solution of underdetermined linear equations by stagewise orthogonal matching pursuit’. Technical Report, 2006.
    26. 26)
      • 16. Xianci, X.: ‘Real time implementation issue in wide band electronic reconnaissance systems’, Electron. Countermeas. Technol., 2004, 19, (3), pp. 36.
    27. 27)
      • 18. Mishali, M., Yonina, C.E.: ‘From theory to practice: sub-Nyquist sampling of sparse wideband analog signals’, IEEE J. Sel. Top. Signal Process., 2010, 4, (2), pp. 375391 (doi: 10.1109/JSTSP.2010.2042414).
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-spr.2012.0086
Loading

Related content

content/journals/10.1049/iet-spr.2012.0086
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address