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

Recent developments in detection, imaging and classification for airborne maritime surveillance

Recent developments in detection, imaging and classification for airborne maritime surveillance

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

Buy article PDF
£12.50
(plus tax if applicable)
Add to cart
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)
Add to cart

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.

© The Institution of Engineering and Technology
Published

The role of maritime patrol missions is to monitor large oceanic areas. The authors focus on a complete signal-processing sequence from the primary detection of the targets to their classification or recognition from an airborne radar. Contrary to the classical approach in which detection, tracking, imaging and classification are considered separately, here the authors propose an integrated strategy based on a close cooperation among all of them. Recent developments in high range resolution target detection are presented and their integration in the complete system is discussed to limit false alarms. High-resolution ISAR imaging of ships is then tackled and associated with a feature extraction process and a support vector machine classifier. A set of real data is used to illustrate the imaging and classification results.

Inspec keywords: target tracking; synthetic aperture radar; airborne radar; support vector machines; search radar; feature extraction

Other keywords: airborne maritime surveillance detection; signal-processing sequence; airborne radar; target detection; airborne maritime surveillance classification; airborne maritime surveillance imaging; maritime patrol missions; feature extraction; support vector machine classifier; oceanic area monitoring; high-resolution ISAR ship imaging

Subjects: Optical, image and video signal processing; Military detection and tracking systems; Radar equipment, systems and applications

References

    1. 1)
    2. 2)
      • Jim's Shipping Website: http://www.jimsshippingwebsite.co.uk/bristol.htm.
    3. 3)
      • N. Bon , A. Khenchaf , R. Garello . GLRT-detection for range- and Doppler-distributed targets in non-Gaussian clutter. IEEE Trans. Aerosp. Electron. Syst. , 2 , 678 - 696
    4. 4)
    5. 5)
      • Bryant, M., Garber, F.: `SVM classifier applied to the MSTAR public data set', Algorithms for Synthetic Aperture Radar Imagery VI - Proc. SPIE, 1999, p. 355–360, 3721.
    6. 6)
      • W.G. Carrara , R.S. Goodman , R.M. Majewski . (1995) Spotlight synthetic aperture radar: signal processing algorithms, ser. ‘remote sensing library.
    7. 7)
      • D. Andersh , M. Hazlett , S. Lee , D. Reeves , D. Sullivan , Y. Chu . Xpatch: a high-frequency electromagnetic scattering prediction code and environment for complex three-dimensional objects. IEEE Antennas Propag. Mag. , 1 , 65 - 69
    8. 8)
      • Moruzzis, M., Saulais, P., Tat, T., Huei, T.: `Automatic target classification for naval radar', Int. Conf. Radar Systems, RADAR'04, 12–22 October 2004, Toulouse, France.
    9. 9)
      • S. Theodoridis , K. Koutroumbas . (2009) Pattern recognition.
    10. 10)
      • Menon, M.: `An automatic ship classification system for ISAR imagery', Proc. Applications and Science of Artificial Neural Networks, 1995, p. 373–388, 2492Proc. SPIE, .
    11. 11)
      • E. Conte , M. Longo . Characterisation of radar clutter as a spherically invariant random process. IEE. Proc. F , 2 , 191 - 197
    12. 12)
      • F. Gini , A. Farina . Vector subspace detection in compound-Gaussian clutter part II: performance analysis. IEEE Aerosp. Electron. Syst. Mag. , 4 , 1312 - 1323
    13. 13)
      • K. Ward . Compound representation of high resolution sea clutter. Electron. Lett. , 16 , 561 - 563
    14. 14)
      • E. Conte , De Maio , G. Ricci . CFAR detection of distributed targets in non-Gaussian disturbance. IEEE Trans. Aerosp. Electron. Syst. , 2 , 612 - 621
    15. 15)
    16. 16)
    17. 17)
      • C. Zahn , R. Roskies . Fourier descriptors for plane closed curves. IEEE Trans. Comput. , 3 , 269 - 281
    18. 18)
      • P. Lacomme , J.-P. Hardange , J.-C. Marchais , E. Normant . (2001) Air and spaceborne radar systems.
    19. 19)
    20. 20)
    21. 21)
    22. 22)
    23. 23)
      • Q. Zhao , J. Principe . Support vector machines for SAR automatic target recognition. IEEE Trans. Aerosp. Electron. Syst. , 2 , 643 - 654
    24. 24)
      • F. Robey , D. Fuhrmann , E. Kelly , R. Nitzberg . A CFAR adaptive matched filter detector. IEEE Aerosp. Electron. Syst. Mag. , 1 , 208 - 216
    25. 25)
      • R. Paulraj , T. Kailath . ESPRIT – a subspace rotation approach to esimation of parameters of cisoids in noise. IEEE Trans. Acoust., Speech, Signal Process. , 1340 - 1342
    26. 26)
    27. 27)
      • F. Gini , A. Farina . Vector subspace detection in compound-Gaussian clutter part I: survey and new results. IEEE Aerosp. Electron. Syst. Mag. , 4 , 1295 - 1311
    28. 28)
      • S. Musman , D. Kerr , C. Bachmann . Automatic recognition of ISAR ship images. IEEE Trans. Antennas Propag. , 4 , 1392 - 1404
    29. 29)
      • P. Swerling . Probability of detection for fluctuating targets. IEEE Trans. Inf. Theory , 2 , 269 - 308
    30. 30)
      • S.S. Blackman . (1986) Multiple-target tracking with radar application.
    31. 31)
      • Gagnon, L., Klepko, R.: `Hierarchical classifier design for airborne SAR images of ships', SPIE Proc. Conf. Automatic Target Recognition VIII, 1998, Orlando.
    32. 32)
      • Knapskog, A.: `Automatic classification of small ships in ISAR images using 3D models and silhouette matching', EUSAR, 2006.
    33. 33)
    34. 34)
      • M. Nakagami . (1960) The m-distribution, a general formula of intensity distriubtion of rapid fading.
    35. 35)
      • R.O. Duda , P.E. Hart , D.G. Stork . Pattern classification.
    36. 36)
      • Radoi, E., Totir, F., Quinquis, A., Anton, L.: `Superresolution imagery based SVM classification of radar targets', EUSAR, 2006.
    37. 37)
      • Bon, N., Khenchaf, A., Quellec, J., Garello, R.: `GLRT-detection for range- and Doppler-distributed targets in non-Gaussian clutter', In Int. Conf. Radar CIE'06, 16–19 October 2006, Shangai, China.
    38. 38)
    39. 39)
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-spr_20070082
Loading

Related content

content/journals/10.1049/iet-spr_20070082
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address