Target recognition and anti-interference technology have become key technologies that determine the performance of precision guided weapons. Aiming at the combat characteristics of infrared air-to-air missiles in complex confrontation scene, this paper analyzes its target recognition and anti-interference development needs, and proposes a hybrid intelligent anti-interference algorithm that combines traditional algorithms and deep learning. This algorithm makes full use of the high reliability advantages of traditional algorithms in certain scene and the high-dimensional feature extraction capabilities of deep learning algorithms in complex scene, maximizing the mining of missile detection scene information, which is of great significance for improving the system’s anti-interference capability. On this basis, an air combat data set for algorithm testing and training is constructed, covering typical air combat scene. The experimental results show that under the same feature fusion conditions, the full anti-interference probability of the hybrid intelligent anti-jamming algorithm in typical scene reaches 71.56%, which is 15.77% higher than the traditional algorithm, which verifies the effectiveness of the algorithm.
