Dim and Weak Target Detection Technology Based on Multi-Characteristic Fusion

Abstract

As the signal of small weak target is weak, its characteristic information is prone to weakening and loss and is difficult to maintain stability. In this paper, a self-learning mechanism is built using continuous signal correlative characteristics. The method is based on K-L transform fusion technology and D-S theory of decision-making. Meanwhile, the fusion technology of “fusion after the first decision-making” and “decision-making after first fusion” organic integration is developed. To verify the detection capability of this technology for small dim targets, a new type of tracking processor is developed. It processes more than one hundred groups of raw image data divided into two groups. The image data is taken by a space target surveillance telescope. Experimental results show that dim targets can be detected and tracked stably and reliably using this technology. Also provided with a complex background suppression and anti-jamming capability, a target can be detected stably and reliably when its SNR is about 1.2.