Aiming at the practical problems such as background clutter interference and occlusion during the tracking of small air targets by infrared imaging guided missiles, we propose an improved SiamFC infrared small target tracking algorithm. By mapping the original spatial data to a high-dimensional Hilbert space, the nonlinear mapping is implicit in the linear learner. This method has the advantages of learnability, efficient calculation, linearizability, and good generalization performance. The target tracking problem provides a new and effective approach with the actual needs of the application. We propose a Siamese web tracker (Att-Siam). Att-Siam fuses convolutional channel attention mechanism, stacked channel attention mechanism and spatial attention mechanism to improve tracking performance. Feature extraction of target objects is enhanced by fusing the channel attention mechanism and two convolution blocks at low convolutional layers. Compared with the benchmark algorithm SiamFC, the improved algorithm is tested on the infrared air weak and small target data set, and the tracking success rate and accuracy are increased by 32.3% and 20.9% respectively. The experimental results show that the proposed algorithm can adapt to complex and diverse infrared air scenes, and achieve effective and stable real-time tracking of small infrared air targets.

Aiming at the problem that traditional design methods make it difficult to control the polarization aberration distribution of optical systems quickly and accurately, this study proposes an automatic optimization design method for polarization optical systems based on deep learning. The unsupervised training model based on ray tracing and polarized ray tracing was constructed by learning the reference lens structural feature data from the optical lens library, and the generalization ability of the deep neural network model was improved to achieve the automatic optimization design of the polarized optical system. The design results show that the optical system structure optimized by the network model is close to the reference lens in the full field of view and the full spectrum and that the optical system structure can be designed for different focal length requirements. The success rate of 1 million groups of initial structures designed is better than 96.403%, and the polarization effect of the optical system is effectively controlled. The proposed deep learning approach to optical design provides a new solution for future complex optical systems and also provides an effective way to improve the design accuracy of special optical systems such as polarization optical systems.