A MoLC+MoM-based G⁰ distribution parameter estimation method with application to synthetic aperture radar target detection

Abstract

The accuracy of background clutter model is a key factor which determines the performance of a constant false alarm rate (CFAR) target detection method. G⁰ distribution is one of the optimal statistic models in the synthetic aperture radar (SAR) image background clutter modeling and can accurately model various complex background clutters in the SAR images. But the application of the distribution is greatly limited by its disadvantages that the parameter estimation is complex and the local detection threshold is difficult to be obtained. In order to solve the above-mentioned problems, an synthetic aperture radar CFAR target detection method using the logarithmic cumulant (MoLC) + method of moment (MoM)-based G⁰ distribution clutter model is proposed. In the method, G⁰ distribution is used for modeling the background clutters, a new MoLC+MoM-based parameter estimation method coupled with a fast iterative algorithm is used for estimating the parameters of G⁰ distribution and an exquisite dichotomy method is used for obtaining the local detection threshold of CFAR detection, which greatly improves the computational efficiency, detection performance and environmental adaptability of CFAR detection. Experimental results show that the proposed SAR CFAR target detection method has good target detection performance in various complex background clutter environments.

Keywords

synthetic aperture radar (SAR) / target detection / statistical modeling / parameter estimation / method of logarithmic cumulant (MoLC)

References

[1] LIY, WANGS-x, JIK-f, SUYi. A new method of automatic target discrimination in high-resolution SAR image [J]. Journal of Nat Univ of Defense Technol, 2007, 29(3): 81-84

[2] KULTIKKADS, CHELLAPPAR. Non-Gaussian CFAR techniques for target detection in high resolution SAR images [C]// Int Conf in Image Processing. Washington, DC: IEEE, 1995910-914

[3] SALAZARJ S, HUSHD RStatistical modeling of target and clutter in single-look non-polarimetric SAR imagery [C]// Proceedings of IASTED Int Conf on Signal and Image Processing., 1998272-276

[4] FRERYA C, MULLERH J, YANASSEC F, SANT’A S. A model for extremely heterogeneous clutter [J]. IEEE Trans on Geosci Remote Sens, 1997, 35(3): 648-659

[5] FREITASC C, FRERYA C, CORREIAA H. The polari metric G distribution for SAR data analysis [J]. Environmetries, 2005, 16(1): 13-31

[6] ELTOFTT. Modeling the amplitude statistics of ultrasonic images [J]. IEEE Trans on Medical Imaging, 2006, 25(2): 229-240

[7] GRECOM S, GINIF. Statistical analysis of high-resolution SAR ground clutter data [J]. IEEE Trans on Geosci Remote Sens, 2007, 45(3): 566-575

[8] SILVAM, CRIBARI-NETOF, FRERYA C. Improved likelihood inference for the roughness parameter of the GA0 distribution [J]. Environmetrics, 2008, 19(4): 347-368

[9] ZHUZ-wei. Shipborne radar maneuvering target tracking based on the variable structure adaptive grid interacting multiple model [J]. Journal of Zhejiang University: Sci C, 2013, 14(9): 733-742

[10] DCVOREM D, O’SULLIVANJ A. Statistical assemmment of model fit for synthetic aperture radar data [J]. SPIE, 2001, 4382: 379-388

[11] GAOG, LIUL, ZHAOL-j, SHIG-t, KUANGG-yao. An adaptive and fast CFAR algorithm based on automatic censoring for target detection in high-resolution SAR images [J]. IEEE Trans on Geosci Remote Sens, 2009, 47(6): 1685-1697

[12] NOVAKL M, HESSES R. On the performance of order-statistics CFAR detectors [C]// IEEE 25th Asilomar Conf Signals, Syst, Comput Pacific Grove. CA: IEEE, 1991835-840

[13] GIERULLC H, SIKANETAI C. Estimating the effective number of looks in interferometric SAR data [J]. IEEE Trans on Geosci Remote Sens, 2002, 40(8): 1733-1742

[14] ABDELFATTAHR, NICOLASJ M. Interferometric SAR coherence magnitude estimation using second kind statistics [J]. IEEE Trans on Geosci Remote Sens, 2006, 44(7): 1942-1953

[15] LIW-z, KUX-s, CHENGJ-h, WANGJ-chao. A new G0 distribution parameter estimation method for SAR images [C]// The 11th National Radar Academic Conference. Beijing: NRAC, 2010210-214

[16] TISONC, NICOLASJ M, TUPINF, MAITREH. A new statistical model for Markovian classification of urban areas in high-resolution SAR images [J]. IEEE Trans on Geosci Remote Sens, 2004, 42(10): 2046-2057

[17] ANFINSENS, ELTOFTT. Application of the matrix-variate Mellin transform to analysis of polarimetric radar images [J]. IEEE Trans on Geosci Remote Sens, 2011, 49(6): 2281-2295

[18] WANGL-x, FANGD-z, ZHANGM-yong. Handbook of mathematics [M]. Beijing: Higher Education Press, 2005352-354