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Approved HG-CFAR Method for Infrared Small Target Detection

An approved CFAR (Constant False Alarm Rate) method based on half side Gaussian model (HG-CFAR) is presented for the target segmentation in infrared image. Firstly, the distribution of the residual image after preprocessing based on morphological filters is exploited. Although widely used and useful, the traditional Gaussian distribution does not appear to be the best choice for modeling the residual images. Half side Gaussian distribution (HG) is adopted to model the residuals since it fits the data better. Then, based on the HG model a new CFAR threshold method called HG-CFAR is proposed. Secondly, a novel algorithm for model parameter estimation is given. The estimation algorithm has at least two merits: one is very simple and efficient can be realized on hardware easily. Another is robust to the number and size of the targets. At last, comparisons are made between HG-CFAR and traditional CFAR based on Gaussian distribution. The results show that the new method is more efficient.