Infrared Dual-band Point Target Detection Based on Gradient Convolution Template and CFAR Criterion

This paper proposes a new dual-band point target detection method based on gradient convolution template and constant false alarm rate (CFAR) criterion. First, we analyze the single-band target detection theory, and then the dual-band detection model is derived according to the AND fusion rule. The threshold signal-to-noise ratio (TNR) is constantly adjusted to obtain the optimal fused probability of detection. Next, the gradient convolution template is employed to suppress complex background. The candidate points are obtained after the CFAR criterion under dual-band model. And finally the point target is successfully detected out through the data fusion correlation of two channels. The simulation and experimental results prove that the proposed dual-band detection method has a good performance to suppress complex background, and the algorithm is simple which meets the requirements of real-time for engineering.

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