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De-ghosting of tomographic images in a radar network with sparse angular sampling

Published online by Cambridge University Press:  15 September 2010

Angie Fasoula*
Affiliation:
Thales Nederland BV, Delft, The Netherlands.
Hans Driessen
Affiliation:
Thales Nederland BV, Hengelo, The Netherlands.
Piet van Genderen
Affiliation:
IRCTR, Delft University of Technology, Delft, The Netherlands.
*
Corresponding author: A. Fasoula Email: angie.fasoula@thalesgroup.com

Abstract

Taking into account sparsity of the reflectivity function of several radar targets of interest, efficient low-complexity automatic target recognition (ATR) systems can be designed. Such ATR systems would be based on two-dimensional (2D) spatial target models of low dimensionality, where critical information on the radar target signature is summarized. Discrete 2D radar target models can be estimated using high range resolution (HRR) data, measured at a sparse system of view angles. This being the main objective, incoherent tomographic processing of HRR data from a distributed surveillance system, made up of several radar nodes, is studied in this paper. A sparse angular sampling scheme is proposed, which exploits diversity due to both the distributed radar system and the target motion. The novelty is in the exploitation of this locally dense, but otherwise sparse set of viewing angles of the targets, obtained using a sparse network of radars. The de-ghosting efficiency of such a sampling scheme is demonstrated geometrically. This results in identification of minimal information resources for unambiguous estimation of a 2D target model, useful for radar target classification.

Type
Original Article
Copyright
Copyright © Cambridge University Press and the European Microwave Association 2010

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References

REFERENCES

[1]Griffiths, H.; Baker, C.: Fundamentals of tomography and radar, Advances in Sensing with Security Applications, Springer Netherlands, 2006.Google Scholar
[2]Desai, M.D.; Jenkins, W.K.: Convolution backprojection image reconstruction for spotlight mode synthetic aperture radar, IEEE Trans. Image Process., 1(4) (1992), 505517.Google Scholar
[3]Carrara, W.G.; Goodman, R.S.; Majewski, R.M.: Spotlight Synthetic Aperture Radar: Signal Processing Algorithms, Artech House, Boston, 1995.Google Scholar
[4]Fasoula, A.; Driessen, H.; van Genderen, P.: 2D parametric target model estimation using HRR data from a radar network, in Proc. Int. Radar Conf. 2009, Bordeaux, October 2009.Google Scholar
[5]Chevalier, F.L.: Principles of Radar and Sonar Signal Processing, Artech House, Boston, London, 2002.Google Scholar
[6]Baraniuk, R.; Steeghs, P.: Compressive radar imaging, in Proc. IEEE Radar Conf. 2007, Waltham, MA, 2007.Google Scholar
[7]Candes, E.; Romberg, J.; Tao, T.: Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information, IEEE Trans. Inform. Theory, 52(2) (2006), 489509.Google Scholar
[8]Edde, B.: Radar Principles, Technology, Applications, Prentice Hall Inc., New Jersey, US, 1993.Google Scholar
[9]Tigrek, R.F.; Heij, W.; van Genderen, P.: Multi-carrier radar waveform schemes for range and doppler processing, In Proc. IEEE Radar Conf. 2009, Pasadena, May 2009.Google Scholar
[10]Moses, R.L.; Potter, L.C.; Cetin, M.: Wide-angle SAR imaging, In SPIE Defence and Security Symp., Algorithms for Synthetic Aperture Radar Imagery XI, April 2004.Google Scholar
[11]Stojanovic, I.; Cetin, M.; Karl, W.C.: Joint space aspect reconstruction of wide-angle SAR exploiting sparsity, In Zelnio, E.G.; Garber, F.D. (Eds.), SPIE Defence and Security Symp., Algorithms for Synthetic Aperture Radar Imagery XV, Orlando, FL, 2008.Google Scholar