http://iet.metastore.ingenta.com
1887

Efficient TR-TBD algorithm for slow-moving weak multi-targets in heavy clutter environment

Efficient TR-TBD algorithm for slow-moving weak multi-targets in heavy clutter environment

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Signal Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

In this study, the authors present an efficient time-dimension-reduced track-before-detect (TR-TBD) processor for slow-moving weak multi-targets detection in strong clutter environment. In their proposed framework, they elaborate observations from multiple frames (or scans) and resample them in time direction, then distinguish the slow-moving targets from the clutter in the radon parameter domain by exploiting the fact that different velocities of targets have different skewing angles corresponding to their tracks in the range–time (range–pulse) plane. To further enlarge the skewing angles differences between the slow-moving targets and the clutter, TR-TBD is proposed by incorporating the time-dimension reduction operator. This is very helpful to amplify the skewing angle of slow-moving targets, while the improvement is very small for the clutter. Therefore, it is much convenient to figure out the slow-moving weak targets from heavy clutter environment based on their amplified skewing angle differences by setting proper threshold. After detecting the targets, CLEAN-based track recovery method is proposed to eliminate the false tracks and recover the true tracks. Experimental results on real-data demonstrate that the proposed algorithm can detect the closely spaced targets and eliminate the false tracks under low signal-to-noise ratio and signal-to-clutter ratio.

References

    1. 1)
      • 1. Bar-Shalom, Y., Blair, W.D.: ‘Multitarget–multisensor tracking: applications and advances’ (Artech House, USA, 2000).
    2. 2)
      • 2. Buzzi, S., Lops, M., Venturino, L., et al: ‘Track-before-detect procedures in a multi-target environment’, IEEE Trans. Aerosp. Electron. Syst., 2008, 44, (3), pp. 11351150.
    3. 3)
      • 3. Su, H.T., Shui, P.L., Liu, H.W., et al: ‘Rao–Blackwellised particle filter based track-before-detect algorithm’, IET Signal Process., 2008, 2, (2), pp. 169176.
    4. 4)
      • 4. Lu, J., Shui, P.L., Su, H.T.: ‘Track-before-detect method based on cost-reference particle filter in non-linear dynamic systems with unknown statistics’, IET Signal Process., 2014, 8, (1), pp. 8594.
    5. 5)
      • 5. Reed, I.S., Gagliardi, R.M., Shao, H.M.: ‘Application of three dimensional filtering to moving target detection’, IEEE Trans. Aerosp. Electron. Syst., 1983, 19, (6), pp. 898905.
    6. 6)
      • 6. Tonissen, S.M., Evans, R.J.: ‘Performance of dynamic programming techniques for track-before-detect’, IEEE Trans. Aerosp. Electron. Syst., 1996, 32, (4), pp. 14401451.
    7. 7)
      • 7. Carlson, B.D., Evans, E.D., Wilson, S.L.: ‘Search radar detection and track with the Hough transform. Part I: system concept’, IEEE Trans. Aerosp. Electron. Syst., 1994, 30, (1), pp. 102108.
    8. 8)
      • 8. Moyer, L.R., Spak, J., Lamanna, P.: ‘A multi-dimensional Hough transform-based track-before-detect technique for detecting weak targets in strong clutter backgrounds’, IEEE Trans. Aerosp. Electron. Syst., 2011, 47, (4), pp. 30623068.
    9. 9)
      • 9. Carretero-Moya, J., Gismero-Menoyo, J., Asensio-Lopez, A., et al: ‘Application of the radon transform to detect small targets in sea clutter’, IET Radar Sonar Navig., 2009, 3, (2), pp. 155166.
    10. 10)
      • 10. Vo, B.-N., Vo, B.-T., Pham, N.-T., et al: ‘Joint detection and estimation of multiple objects from image observations’, IEEE Trans. Signal Process., 2010, 58, (10), pp. 51295141.
    11. 11)
      • 11. Grossi, E., Lops, M., Venturino, L.: ‘A novel dynamic programming algorithm for track-before-detect in radar systems’, IEEE Trans. Signal Process., 2013, 61, (10), pp. 26082619.
    12. 12)
      • 12. Grossi, E., Lops, M., Venturino, L.: ‘A heuristic algorithm for track-before-detect with thresholded observations in radar systems’, IEEE Signal Process. Lett., 2013, 20, (8), pp. 811814.
    13. 13)
      • 13. Grossi, E., Lops, M., Venturino, L.: ‘A track-before-detect algorithm with thresholded observations and closely-spaced targets’, IEEE Signal Process. Lett., 2013, 20, (12), pp. 11711174.
    14. 14)
      • 14. Yi, W., Morelande, M.R., Kong, L.J., et al: ‘An efficient multi-frame track-before-detect algorithm for multi-target tracking’, IEEE J. Sel. Top. Signal Process., 2013, 7, (3), pp. 421434.
    15. 15)
      • 15. Toft, P.: ‘The radon transform-theory and implementation’. PhD thesis, Department of Mathematical Modelling, Technical University of Denmark, 1989.
    16. 16)
      • 16. Martorella, M., Acito, N., Berizzi, F.: ‘Statistical CLEAN technique for ISAR imaging’, IEEE Trans. Geosci. Remote Sens., 2007, 45, (11), pp. 35523560.
    17. 17)
      • 17. Hsiao, J.K.: ‘On the optimization of MTI clutter rejection’, IEEE Trans. Aerosp. Electron. Syst., 1974, 10, (5), pp. 622629.
    18. 18)
      • 18. Rohling, H.: ‘Radar CFAR thresholding in clutter and multiple target situations’, IEEE Trans. Aerosp. Electron. Syst., 1983, 19, (4), pp. 608621.
    19. 19)
      • 19. Xu, X., Wei, X.H., Ye, Z.F.: ‘DOA estimation based on sparse signal recovery utilizing weighted L1-norm penalty’, IEEE Signal Process. Lett., 2012, 19, (3), pp. 155158.
    20. 20)
      • 20. Luo, F., Zhang, D.T., Zhang, B.: ‘The fractal properties of sea clutter and their applications in maritime target detection’, IEEE Geosci. Remote Sens. Lett., 2013, 10, (6), pp. 12951299.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-spr.2016.0367
Loading

Related content

content/journals/10.1049/iet-spr.2016.0367
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address