volume | PIER Journals

Abstract

Recent research has shown that the Pareto family of distributions provides suitable intensity models for high resolution X-band maritime surveillance radar clutter. In particular, the two parameter Pareto Type II model has been shown to fit the Australian Defence Science and Technology Group's medium to high grazing angle clutter returns very well. The Pareto Type II model is a special case of a Burr distributional model, which is a three parameter power law statistical model. Hence this paper begins by investigating the fitting of the Burr model to real data. Based upon these results a detailed study of the development of non-coherent sliding window detectors is justified, for operation in such clutter. Several different approaches will be applied to construct the decision rules. These include a transformation approach and direct adaptation of such detectors, designed for operation in exponentially distributed clutter, to the Burr clutter setting. In addition to this, the fact that the Burr distribution is invariant with respect to two of its distributional parameters allows specication of detection processes which have the constant false alarm rate property with respect to these model parameters. Performance analysis, in simulated clutter, of the derived detectors is then examined. This includes performance in the presence of interference and false alarm regulation during clutter power transitions. This is complemented by an application of the decision rules to target detection in real high resolution X-band maritime surveillance radar clutter.

1. Minkler, G. and J. Minkler, CFAR: The Principles of Automatic Radar Detection in Clutter, Magellan, Baltimore, 1990.

2. Weinberg, G. V., Radar Detection Theory of Sliding Window Processes, CRC Press, 2017, ISBN 9781498768184.
doi:10.1201/9781315154015

3. Balleri, A., A. Nehorai, and J. Wang, "Maximum likelihood estimation for compound-gaussian clutter with inverse-Gamma texture," IEEE Transactions on Aerospace and Electronic Systems, Vol. 43, 775-779, 2007.
doi:10.1109/TAES.2007.4285370 Google Scholar

4. Farshchian, M. and F. L. Posner, "The Pareto distribution for low grazing angle and high resolution X-band sea clutter," IEEE Radar Conference Proceedings, 789-793, 2010. Google Scholar

5. Weinberg, G. V., "Assessing Pareto fit to high resolution high grazing angle sea clutter," IET Electronics Letters, Vol. 47, 516-517, 2011.
doi:10.1049/el.2011.0518 Google Scholar

6. Burr, I.W., "Cumulative frequency functions," Annals of Mathematical Statistics, Vol. 13, 215-232, 1942.
doi:10.1214/aoms/1177731607 Google Scholar

7. Stacy, N. J. S., M. P. Burgess, M. R. Muller, and R. Smith, "Ingara: An integrated airborne imaging radar system," Proceedings of the International Geoscience and Remote Sensing Symposium, 1618-1620, 1996. Google Scholar

8. Stacy, N., D. Crisp, A. Goh, D. Badger, and M. Preiss, "Polarimetric analysis of fine resolution X-band sea clutter data," Proceedings of the International Geoscience and Remote Sensing Symposium, 2787-2790, 2005. Google Scholar

9. Crisp, D. J., N. J. S. Stacy, D. A. Hudson, P. B. Pincus, and A. S. Goh, "Polarimetric analysis of maritime SAR data collected with the DSTO ingara X-band radar," Proceedings of the International Geoscience and Remote Sensing Symposium, 3870-3873, 2007. Google Scholar

10. Crisp, D. J., L. Rosenberg, N. J. Stacy, and Y. Dong, "Modelling X-band sea clutter with the K-distribution: Shape parameter variation," Proceedings of the International Radar Conference-Surveillance for a Safer World, 1-6, 2009. Google Scholar

11. Weinberg, G. V., "Constant false alarm rate detectors for Pareto clutter models," IET Radar, Sonar and Navigation, Vol. 7, 153-163, 2013.
doi:10.1049/iet-rsn.2011.0374 Google Scholar

12. Weinberg, G. V., "General transformation approach for constant false alarm rate detector development," Digital Signal Processing, Vol. 30, 15-26, 2014.
doi:10.1016/j.dsp.2014.04.010 Google Scholar

13. Weinberg, G. V., "Management of interference in Pareto CFAR processes using adaptive test cell analysis," Signal Processing, Vol. 104, 264-273, 2014.
doi:10.1016/j.sigpro.2014.04.025 Google Scholar

14. Weinberg, G. V., "Development of an improved minimum order statistic detection process for Pareto distributed clutter ," IET Radar, Sonar and Navigation, Vol. 9, 19-30, 2015.
doi:10.1049/iet-rsn.2013.0371 Google Scholar

15. Weinberg, G. V., "Examination of classical detection schemes for targets in Pareto distributed clutter: Do classical CFAR detectors exist, as in the Gaussian case?," Multidimensional Systems and Signal Processing, Vol. 26, 599-617, 2015.
doi:10.1007/s11045-013-0275-y Google Scholar

16. Weinberg, G. V., "On the construction of CFAR decision rules via transformations," IEEE Transactions on Geoscience and Remote Sensing, Vol. 55, 1140-1146, 2017.
doi:10.1109/TGRS.2016.2620138 Google Scholar

17. Finn, H. M. and R. S. Johnson, "Adaptive detection model with threshold control as a function of spatially sampled clutter-level estimates," RCA Review,, Vol. 29, 414-464, 1968. Google Scholar

18. Gandhi, P. P. and S. A. Kassam, "Analysis of CFAR processors in nonhomogeneous background," IEEE Transactions on Aerospace and Electronic Systems, Vol. 24, 427-445, 1988.
doi:10.1109/7.7185 Google Scholar

19. Weinberg, G. V., "An invariant sliding window detection process," IEEE Signal Processing Letters, Vol. 24, 1093-1097, 2017.
doi:10.1109/LSP.2017.2710344 Google Scholar

20. Beaumont, G. P., Intermediate Mathematical Statistics, Chapman and Hall, 1980.
doi:10.1007/978-94-009-5794-7

21. Ross, S. M., Simulation, 5th Ed., Academic Press, 2012.

22. Weinberg, G. V., "Estimation of Pareto clutter parameters using order statistics and linear regression," IET Electronics Letters, Vol. 49, 845-846, 2013.
doi:10.1049/el.2013.0916 Google Scholar

23. Jakeman, E. and P. N. Pusey, "A model for non-Rayleigh sea echo," IEEE Transactions on Antennas and Propagation, Vol. 24, 806-814, 1976.
doi:10.1109/TAP.1976.1141451 Google Scholar

24. Ward, K. D., "Compound representation of high resolution sea clutter," IEE Electronics Letters, Vol. 17, 561-563, 1981.
doi:10.1049/el:19810394 Google Scholar

25. Watts, S., "Radar detection prediction in sea clutter using the compound K-distribution model," IEE Proceedings F, Vol. 132, 613-620, 1985. Google Scholar

26. Weibull, W., "A statistical distribution function of wide applicability," ASME Journal of Applied Mechanics, Vol. 18, 293-297, 1951. Google Scholar

27. Sekine, M. and Y. Mao, Weibull Radar Clutter, IET, UK, 1990.
doi:10.1049/PBRA003E

28. Tao, D., S. N. Anfinsen, and C. Brekke, "Robust CFAR detector based on truncated statistics in multiple-target situations ," IEEE Transactions on Geoscience and Remote Sensing, Vol. 54, 117-134, 2016.
doi:10.1109/TGRS.2015.2451311 Google Scholar

29. Tao, D., A. P. Doulgeris, and C. Brekke, "A segmentation-based CFAR detection algorithm using truncated statistics," IEEE Transactions on Geoscience and Remote Sensing, Vol. 54, 2887-2898, 2016.
doi:10.1109/TGRS.2015.2506822 Google Scholar

30. Dai, H., L. Du, Y. Wang, and Z. Wang, "A modified CFAR algorithm based on object proposals for ship target detection in SAR images," IEEE Geoscience and Remote Sensing Letters, Vol. 13, 1925-1929, 2016.
doi:10.1109/LGRS.2016.2618604 Google Scholar

31. Gao, G. and G. Shi, "CFAR ship detection in nonhomogeneous sea clutter using polarimetric SAR data based on the notch filter," IEEE Transactions on Geoscience and Remote Sensing, Vol. 55, 4811-4824, 2017.
doi:10.1109/TGRS.2017.2701813 Google Scholar

32. Izzo, A., M. Liguori, C. Clemente, C. Galdi, M. Di Bisceglie, and J. J. Soraghan, "Multimodel CFAR detection in foliage penetrating SAR images," IEEE Transactions on Aerospace and Electronic Systems, Vol. 53, 1769-1780, 2017.
doi:10.1109/TAES.2017.2672018 Google Scholar

33. Wang, C., F. Bi, W. Zhang, and L. Chen, "An intensity-space domain CFAR method for ship detection in HR SAR images," IEEE Geoscience and Remote Sensing Letters, Vol. 14, 529-533, 2017.
doi:10.1109/LGRS.2017.2654450 Google Scholar

34. Ai, J., X. Yang, J. Song, Z. Dong, L. Jia, and F. Zhou, "An adaptively truncated clutter-statistics-based two-parameter CFAR detector in SAR imagery," IEEE Journal of Oceanic Engineering, Vol. 43, 267-279, 2018.
doi:10.1109/JOE.2017.2768198 Google Scholar

35. Lu, S., W. Yi, W. Liu, G. Cui, L. Kong, and X. Yang, "Data-dependent clustering-CFAR detector in heterogeneous environment," IEEE Transactions on Aerospace and Electronic Systems, Vol. 54, 476-485, 2018.
doi:10.1109/TAES.2017.2740065 Google Scholar

36. Li, T., Z. Liu, R. Xie, and L. Ren, "An improved superpixel-level CFAR detection method for ship targets in high-resolution SAR images," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 11, 184-194, 2018.
doi:10.1109/JSTARS.2017.2764506 Google Scholar

37. Zhao, W., J. Li, X. Yang, Q. Peng, and J. Wang, "Innovative CFAR detector with effective parameter estimation method for generalised Gamma distribution and iterative sliding window strategy," IET Image Processing, Vol. 12, 60-69, 2018.
doi:10.1049/iet-ipr.2017.0225 Google Scholar

38. Weinberg, G. V., L. Bateman, and P. Hayden, "Constant false alarm rate detection in Pareto Type II clutter ," Digital Signal Processing, Vol. 68, 192-198, 2017.
doi:10.1016/j.dsp.2017.06.014 Google Scholar

Dr. Graham V. Weinberg

Dr. Charlie Tran