In recent years, micro-Doppler target classification technology has been widely used for radar target recognition. However, due to the lack of sufficient data, it has become a challenge to train a model with excellent performance using the transfer learning method. Most of the existing transfer learning methods for micro-Doppler tasks use optical image data or simulation data as the source domain, and the use of fine-tuning as the transfer method makes it difficult to obtain good results. This paper proposes a transfer learning method using speech data as the source domain for micro-Doppler classification tasks. The proposed method uses speech data as the source domain and improves the accuracy of micro-Doppler classification through TCA and deep learning models used jointly. After experimental verification, the proposed method can use the 2.8 M parameters to improve accuracy by more than 5% compared with common methods in the case of a small number of frames, and the proposed method achieves better results with a small number of points.

近年来，微多普勒目标分类技术已广泛用于雷达目标识别。然而，由于缺乏足够的数据，使用转移学习方法来训练具有优异性能的模型已成为挑战。现有的用于微多普勒任务的大多数转移学习方法都使用光学图像数据或模拟数据作为源域，而使用微调作为转移方法将很难获得良好的结果。本文提出了一种将语音数据作为微多普勒分类任务源域的转移学习方法。所提出的方法将语音数据用作源域，并通过联合使用TCA和深度学习模型提高了微多普勒分类的准确性。经过实验验证，提出的方法可以使用2。