基于生成式对抗网络的目标扰动场实时解译与识别

Key Points

• FlowReconNet achieves a reconstruction accuracy comparable to traditional algorithms, indicating its efficacy in target disturbance field interpretation.
• The algorithm processes images at a speed of 67.33 frames per second, significantly faster than conventional techniques.
• Incorporating a multi-value joint loss function optimizes the generator, enhancing the realism of generated disturbance fields.
• Experimental results show an average peak signal-to-noise ratio of 29.948 dB and a classification accuracy of 96.701%.

Abstract