To enhance the long-range detection and recognition capabilities of airborne infrared imaging systems for dynamic targets， a video super-resolution reconstruction method based on a recurrent residual neural network is proposed. This method addresses the degradation process inherent to airborne infrared imaging systems and incorporates motion information from dynamic targets to improve video reconstruction quality through optimization of the network architecture. Initially， the degradation process of infrared video is analyzed， encompassing downsampling， motion blur， and noise interference， leading to the construction of a low-resolution dataset reflective of these factors. Subsequently， the recurrent residual neural network is introduced， which effectively extracts and propagates motion information of dynamic targets， thereby restoring the shape， contours， and intricate details of the targets. A skip-connected residual structure is implemented to enhance the network backbone， ensuring smooth information flow while increasing suitability for processing extended video sequences and effectively mitigating the gradient vanishing problem during training.Furthermore， by adjusting the number of residual blocks and the convolution kernel sizes within each layer， the expressive power and computational efficiency of the network are optimized. Additionally， a novel loss function is proposed， which combines Charbonnier loss and high-frequency information loss （HFLoss） for joint supervision， facilitating improved recovery of high-frequency details in the reconstructed images. Experimental results demonstrate that the proposed method achieves 2 times super-resolution for dynamic targets on various publicly available and experimentally collected infrared datasets， yielding a PSNR exceeding 40 dB and an SSIM above 0.92， with a reconstruction rate of no less than 45 frame/s. Moreover， the system's angular resolution is accurately calibrated utilizing a resolution test target alongside an infrared zoom imaging system， substantiating the advantages of the proposed reconstruction method in enhancing angular resolution， resulting in a 1.43 times increase in system angular resolution. The experimental findings illustrate that the proposed method satisfies the critical requirements for high real-time performance and reconstruction quality in airborne imaging systems.