volume | PIER Journals

Abstract

This paper deals with the problem of constant false alarm rate (CFAR) target detection in high-resolution ground synthetic aperture radar (SAR) images based on KK distribution. For the parameter estimation of KK distribution, the semi-experiential algorithm is analyzed firstly. Then a new estimation algorithm based on the particle swarm optimization (PSO) is proposed, which takes the discrepancies between the histogram of the clutter data and probability density function (PDF) of KK distribution at some selected points as the cost function to search for the optimal parameter values using PSO algorithm. The performance of the two algorithms is compared using Monte-Carlo simulation using the simulated data sets generated under different conditions; and the estimation results validate the better performance of the new algorithm. Then the KK distribution, which is proposed for spiky sea clutter originally, is applied to model the real ground SAR clutter data. The goodness-of-fit test clearly show that the KK distribution is able to model the ground SAR clutter much better than some common used model, such as standard K-distribution and Gamma, etc. On this basis, a global CFAR target detection algorithm is presented. The detection threshold is calculated numerically through the cumulative density function (CDF) of KK distribution. Comparing the amplitude of every SAR image pixel with this threshold, the potential targets in ground SAR images can be located effectively. Then target clustering is implemented to eliminate the false alarm and obtain more accurate target regions. The detection results of the proposed algorithm in a typical ground SAR image show that it has better performance than the detector based on G⁰ distribution.

1. Yan, W., J.-D. Xu, G. Wei, L. Fu, and H.-B. He, "A fast 3D imaging technique for near-field circular SAR processing," Progress In Electromagnetics Research, Vol. 129, 271-285, 2012. Google Scholar

2. An, D. X., Z.-M. Zhou, X.-T. Huang, and T. Jin, "A novel imaging approach for high resolution squinted spotlight SAR based on the deramping-based technique and azimuth NLCS principle," Progress In Electromagnetics Research, Vol. 123, 485-508, 2012.
doi:10.2528/PIER11112110 Google Scholar

3. Guo, D., H. Xu, and J. Li, "Extended wavenumber domain algorithm for highly squinted sliding spotlight SAR data processing," Progress In Electromagnetics Research, Vol. 114, 17-32, 2011. Google Scholar

4. Wei, S.-J., X.-L. Zhang, and J. Shi, "Linear array SAR imaging via compressed sensing," Progress In Electromagnetics Research, Vol. 117, 299-319, 2011. Google Scholar

5. Xu, W., P. Huang, and Y.-K. Deng, "Multi-channel SPCMB-tops SAR for high-resolution wide-swath imaging," Progress In Electromagnetics Research, Vol. 116, 533-551, 2011. Google Scholar

6. Novak, L. M., G. J. Owirka, and A. L. Weaver, "Automatic target recognition using enhanced resolution SAR data," IEEE Transactions on Aerospace and Electronic Systems, Vol. 35, No. 1, 157-175, 1999.
doi:10.1109/7.745689 Google Scholar

7. Wang, Y., Q. Song, T. Jin, X. Huang, and H. Zhang, "A novel minefield detection approach based on morphological diversity," Progress In Electromagnetics Research, Vol. 136, 239-253, 2013. Google Scholar

8. Lou, J., T. Jin, and Z. Zhou, "Feature extraction for landmine detection in UWB SAR via SWD and isomap," Progress In Electromagnetics Research, Vol. 138, 157-171, 2013. Google Scholar

9. Zhai, Y., J. Li, J. Gan, and Z. Ying, "A multi-scale local phase quantization plus biomimetic pattern recognition method for SAR automatic target recognition," Progress In Electromagnetics Research, Vol. 135, 105-122, 2013. Google Scholar

10. Teng, H. T., H.-T. Ewe, and S. L. Tan, "Multifractal dimension and its geometrical terrain properties for classification of multi-band multi-polarized SAR image," Progress In Electromagnetics Research, Vol. 104, 221-237, 2010.
doi:10.2528/PIER10022001 Google Scholar

11. Ren, S., W. Chang, T. Jin, and Z. Wang, "Automated SAR reference image preparation for navigation," Progress In Electromagnetics Research, Vol. 121, 535-555, 2011.
doi:10.2528/PIER11091405 Google Scholar

12. Di Bisceglie, M. and C. Galdi, "CFAR detection of extended objects in high-resolution SAR images," IEEE Transactions on Geoscience and Remote Sensing, Vol. 43, No. 4, 833-843, 2005.
doi:10.1109/TGRS.2004.843190 Google Scholar

13. Ai, J., X. Qi, W. Yu, et al. "A new CFAR ship detection algorithm based on 2-D joint log-normal distribution in SAR images," IEEE Transactions on Geoscience and Remote Sensing Letters, Vol. 7, No. 4, 806-810, 2010.
doi:10.1109/LGRS.2010.2048697 Google Scholar

14. Gao, G., "A parzen-window-kernel-based CFAR algorithm for ship detection in SAR images," IEEE Transactions on Geoscience and Remote Sensing Letters, Vol. 8, No. 3, 557-561, 2011.
doi:10.1109/LGRS.2010.2090492 Google Scholar

15. Habib, M. A., M. Barkat, B. Aissa, and T. A. Denidni, "CA-CFAR detection performance of radar targets embedded in non-centered chi-2 gamma clutter," Progress In Electromagnetics Research, Vol. 88, 135-148, 2008.
doi:10.2528/PIER08092203 Google Scholar

16. Tian, B., D.-Y. Zhu, and Z.-D. Zhu, "A novel moving target detection approach for dual-channel SAR system," Progress In Electromagnetics Research, Vol. 115, 191-206, 2011. Google Scholar

17. Brekke, C. and S. N. Anfinsen, "Ship detection in ice-infested waters based on dual-polarization SAR imagery," IEEE Transactions on Geoscience and Remote Sensing Letters, Vol. 8, No. 3, 391-395, 2011.
doi:10.1109/LGRS.2010.2078796 Google Scholar

18. Cui, Y., G. Zhou, J. Yang, and Y. Yamaguchi, "On the iterative censoring for target detection in SAR images," IEEE Transactions on Geoscience and Remote Sensing Letters, Vol. 8, No. 4, 641-645, 2011.
doi:10.1109/LGRS.2010.2098434 Google Scholar

19. Budillon, A., A. Evangelista, and G. Schirinzi, "GLRT detection of moving targets via multibaseline along-track interferometric SAR systems," IEEE Transactions on Geoscience and Remote Sensing Letters, Vol. 9, No. 2, 348-352, 2012.
doi:10.1109/LGRS.2011.2168381 Google Scholar

20. Zhang, G. and J.-F. Cao, "Application of extended fractal features in target sized objects detection of SAR image," Journal of Nanjing University of Aeronautics and Astronautics, Vol. 36, No. 3, 378-382, 2004. Google Scholar

21. Tello, M., C. Lopez-Martinez, and J. J. Mallorqui, "A novel algorithm for ship detection in SAR imagery based on the wavelet transform," IEEE Transactions on Geoscience and Remote Sensing Letters, Vol. 2, No. 2, 201-205, 2005.
doi:10.1109/LGRS.2005.845033 Google Scholar

22. Zhang, J., G. Gao, D.-F. Zhou, and J.-J. Huang, "Comparison on two CFAR algorithms of vehicle target detection in SAR images," Signal Processing, Vol. 24, No. 1, 78-82, 2008. Google Scholar

23. Greco, M. S. and F. Gini, "Statistical analysis of high-resolution SAR ground clutter data," IEEE Transactions on Geoscience and Remote Sensing, Vol. 45, No. 3, 566-575, 2007.
doi:10.1109/TGRS.2006.888141 Google Scholar

24. Zhao, Y. W., M. Zhang, X. P. Geng, and P. Zhou, "A comprehensive facet model for bistatic SAR imagery of dynamic ocean scene," Progress In Electromagnetics Research, Vol. 123, 427-445, 2012.
doi:10.2528/PIER11100910 Google Scholar

25. Albert, M. D., Y. J. Lee, H. T. Ewe, and H. T. Chuah, "Multilayer model formulation and analysis of radar backscattering from sea ice," Progress In Electromagnetics Research, Vol. 128, 267-290, 2012. Google Scholar

26. Bausssard, A., M. Rochdi, and A. Khenchaf, "PO/Mec-based scattering model for complex objects on a sea surface," Progress In Electromagnetics Research, Vol. 111, 229-251, 2011.
doi:10.2528/PIER10083005 Google Scholar

27. Frery, A. C., H.-J. MÄuller, et al. "A model for extremely heterogeneous clutter," IEEE Transactions on Geoscience and Remote Sensing, Vol. 35, No. 3, 648-659, 1997.
doi:10.1109/36.581981 Google Scholar

28. Gao, G., L. Liu, L. Zhao, G. Shi, and G. Kuang, "An adaptive and fast CFAR algorithm based on automatic censoring for target detection in high-resolution SAR images," IEEE Transactions on Geoscience and Remote Sensing, Vol. 47, No. 6, 1685-1697, 2009.
doi:10.1109/TGRS.2008.2006504 Google Scholar

29. Dong, Y., "Distribution of X-band high resolution and high grazing angle sea clutter,", Defense Science and Technology Organisation, 2006. Google Scholar

30. Ward, K. D., "Compound representation of high resolution sea clutter," Electronics Letters, Vol. 17, No. 16, 561-563, 1981.
doi:10.1049/el:19810394 Google Scholar

31. Watts, S., "Radar detection prediction in K-distributed sea clutter and thermal noise," IEEE Transactions on Aerospace and Electronic Systems, Vol. 23, No. 1, 40-45, 1987.
doi:10.1109/TAES.1987.313334 Google Scholar

32. Skolnik, M. I., Introduction to Radar Systems, 3rd Edition, McGraw-Hill, New York, 2008.

33. Ward, K. D. and R. J. A. Tough, "Radar detection performance in sea clutter with discrete spikes," International Radar Conference, 253-257, 2002. Google Scholar

34. Watts, S., K. D. Ward, and R. J. A. Tough, "The physics and modeling of discrete spikes in radar sea clutter," Proc. of International Radar Conference, 2005. Google Scholar

35. Anfinsen, S. N. and T. Eltoft, "Application of the matrix-variate Mellin transform to analysis of polarimetric radar images," IEEE Transactions on Geoscience and Remote Sensing, Vol. 49, No. 6, 2281-2295, 2011.
doi:10.1109/TGRS.2010.2103945 Google Scholar

36. Kennedy, J. and R. Eberhart, "Particle swarm optimization," Proc. of the IEEE International Conference on Neural Networks, 1942-1948, 1995.
doi:10.1109/ICNN.1995.488968 Google Scholar

37. Cui, X., T. Potok, and P. Palathingal, "Document clustering using particle swarm optimization," Proc. of the 2005 IEEE on Swarm Intelligence Symposium, 185-191, 2005. Google Scholar

38. Ren, Z. and J. Wang, "New adaptive particle swarm optimization algorithm with dynamically changing inertia weight," Computer Science, Vol. 2, No. 36, 227-229, 2009. Google Scholar

39. Shi, Y. and R. C. Eberhar, "Empirical study of particle swarm optimization," Proc. of the 1999 IEEE on Evolutionary Computation, 1945-1948, 1999. Google Scholar

40. Roberts, W. J. J. and S. Furui, "Maximum likelihood estimation of K-distribution parameters via the expectation-maximization algorithm," IEEE Transactions on Signal Processing, Vol. 48, No. 12, 3303-3306, 2000.
doi:10.1109/78.886993 Google Scholar

41. Anastassopoulos, V., G. A. Lampropoulos, A. Drosopoulos, and M. Rey, "High resolution radar clutter statistics," IEEE Transactions on Aerospace and Electronic Systems, Vol. 35, No. 1, 43-60, 1999.
doi:10.1109/7.745679 Google Scholar

42. Li, H.-C., W. Hong, Y.-R. Wu, and P.-Z. Fan, "An efficient and flexible statistical model based on generalized Gamma distribution for amplitude SAR images," IEEE Transactions on Geoscience and Remote Sensing, Vol. 48, No. 6, 2711-2722, 2010.
doi:10.1109/TGRS.2010.2041239 Google Scholar

43. Weinberg, G. V., "An investigation of the Pareto distribution as a model for high grazing angle clutter,", Electronic Warfare-and Radar Division Defense Science and Technology Organization, 2011. Google Scholar

Dr. Yanzhao Gao

Dr. Ronghui Zhan

Dr. Jianwei Wan

Dr. Jiemin Hu

Dr. Jun Zhang