Target detection with synthetic aperture radar

Target detection with synthetic aperture radar (SAR) is considered. We derive generalized likelihood ratio (GLR) detection algorithms that may be used with SAR images that are obtained with coherent subtraction or have Gaussian distributions. We analytically compare the performance of (1) a single pixel detector, (2) a detector using complete knowledge of the target signature information and known orientation information, (3) a detector using incomplete knowledge of the target signature information and known orientation information (4) a detector using unknown target signature information and known orientation information, and (5) a detector using unknown target signature information and unknown orientation information.

[1]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[2]  E. J. Kelly An Adaptive Detection Algorithm , 1986, IEEE Transactions on Aerospace and Electronic Systems.

[3]  David K. Barton,et al.  Modern Radar System Analysis , 1988 .

[4]  Dan R. Sheen,et al.  Ultrawide-bandwidth polarimetric SAR imagery of foliage-obscured objects , 1993, Photonics West - Lasers and Applications in Science and Engineering.

[5]  B. Porat,et al.  Digital Spectral Analysis with Applications. , 1988 .

[6]  Jian Li,et al.  On image and template false alarm rates when using target templates for target detection , 1994, IEEE Signal Processing Letters.

[7]  G. J. Owirka,et al.  Optimal polarimetric processing for enhanced target detection , 1991, NTC '91 - National Telesystems Conference Proceedings.

[8]  Edward J. Kelly Finite-Sum Expressions for Signal Detection Probabilities , 1981 .

[9]  Xiaoli Yu,et al.  Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution , 1990, IEEE Trans. Acoust. Speech Signal Process..