Target detection accuracy improvement in synthetic aperture radar

This paper proposes a decision method to improve the target identification accuracy in a synthetic aperture radar processing algorithm. In the imaging algorithm, Fourier based processing algorithm is used to obtain the processed image from the measured 2D Cartesian backscattered frequency domain data. In practical applications, the measured data is limited in frequency band and aspect angle interval and this leads to radar images with limited resolution. Besides, the noise in measured data affects the target identification accuracy. In this paper, a decision rule is proposed to improve the target identification accuracy by comparing the processed image with the images in the data bank. With the decision rule, one can assign a well known target distribution to the processed image. In this work the effects of noise, frequency and aspect angle limitation in the decision accuracy are investigated and the results are presented.