Abstract:The infrared air target recognition technology under complex jamming conditions is a hot research topic in the field of air combat countermeasures. Complex artificial jamming seriously obscures the target, destroying the continuity and significance of the target characteristics.It’s unable to fully describe the characteristics of the identified object, resulting in the reduction of the accuracy of air target recognition. In order to solve this problem, an anti-jamming recognition algorithm for aerial targets based on the image mixed depth features is proposed. First, the image depth feature is extracted based on the convolution neural network, and the depth feature is effectively fused with the histogram of gradient (HOG) feature to construct the mixed depth feature. In view of the diversity of the confrontation situation between the target and the jamming in the combat scenario, the two-classification model of the support vector machine is improved to the three-classification model to accurately classify the three states of the target, jamming and target jamming adhesion. The experimental results show that the accuracy of the anti-jamming recognition algorithm based on the mixed depth feature is 92.29% in the complex jamming environment. The algorithm can effectively solve the anti-jamming recognition problem when the target is obscured by jamming and forms the jamming adhesion state of the target.