High-resolution Imaging Method for Through-the-wall Radar Based on Transfer Learning with Simulation Samples
This paper addresses the problem of high-resolution imaging of shadowed multiple-targets with limited labeled data,by proposing a transfer-learning-based method for through-the-wall radar imaging.First,a generative adversarial sub-network is developed to facilitate the migration of labeled simulation data to measured data,overcoming the difficulty of generating labeled data.This method incorporates an attention mechanism,adaptive residual blocks,and a multi-scale discriminator to improve the quality of image migration.It also incorporates a structural consistency loss function to minimize perceptual differences between images.Finally,the labeled data are used to train the through-the-wall radar target-imaging sub-network,achieving high-resolution imaging of multiple targets through walls.Experimental results show that the proposed method effectively reduces discrepancies between simulated and obtained images,and generates pseudo-measured images with labels.It systematically addresses issues such as side/grating ghost interference,target image defocusing,and multi-target mutual interference,significantly improving the multi-target imaging quality of the through-the-wall radar.The imaging accuracy achieved is 98.24%,90.97%and 55.17%for single,double,and triple-target scenarios,respectively.Compared with CycleGAN,the imaging accuracy for the corresponding scenarios is improved by 2.29%,40.28%and 15.51%,respectively.
Transfer learningGenerative Adversarial Nets(GAN)Domain adaptationThrough-the-Wall Radar(TWR)High-resolution imaging
万方数据
维普
