基于DNET的空中红外目标抗干扰识别算法

Infrared air-to-air missile anti-interference technology is one of the key technologies to achieve accurate guidance and strike capabilities. Aiming at the practical problems such as shadowing, adhesion, similarity and other interference phenomena caused by artificial interference on aerial infrared targets, and the drastic changes in shape, scale, and radiation characteristics caused by target maneuver and relative motion, this paper proposes an aerial infrared image target anti-interference recognition algorithm based on a feature extraction deep convolutional neural network DNET. Firstly, using dense connections on large-scale feature maps, the DNET network stores the network output of each layer in the front channel. A feature attention mechanism is introduced at the end of the network to obtain the information feature recognition weight of each feature channel. Secondly, a multi-scale dense connection module is added and combined with multi-scale feature fusion detection to improve the ability to extract target features with large-scale changes. Experimental results show that the DNET network can accurately identify the target with the interference of infrared decoy in the process of the infrared target changing from a point target to an imaging target until it fills the field of view. The accuracy, the recall rate, and the recognition speed of DNET reach 99.36%, 96.95%, and 132 fps, respectively, indicating the high recognition accuracy, high recall rate, fast recognition speed, and good robustness of the DNET network.