利用自然噪声对微多普勒频谱图进行去噪
Learning from Natural Noise to Denoise Micro-Doppler Spectrogram
摘要：
近年来，微多普勒分析由于其增强分类策略的能力而变得越来越受欢迎。应用包括识别日常人类活动，区分无人机和鸟类，以及识别不同类型的车辆。然而，嘈杂的时间-频率频谱图可以显着影响分类器的性能，必须使用适当的去噪算法来解决。近年来，深度学习算法催生了许多基于深度神经网络的去噪算法。对于这些方法，噪声建模是最重要的部分，用于辅助训练。在本文中，我们分解的问题，并提出了一种新的去噪方案：首先，生成对抗网络（GAN）是用来学习噪声分布和相关性从现实世界的环境中，然后，模拟器是用来生成干净的微多普勒频谱图，最后，生成的噪声和干净的模拟数据相结合的训练数据，训练卷积神经网络（CNN）去噪。在实验中，我们定性和定量分析这一过程的模拟和测量数据。此外，从自然噪声中学习的思想可以很好地应用于其他现有的框架，并表现出比其他噪声模型更好的性能。
英文摘要：
Micro-Doppler analysis has become increasingly popular in recent years owning to the ability of the technique to enhance classification strategies. Applications include recognising everyday human activities, distinguishing drone from birds, and identifying different types of vehicles. However, noisy time-frequency spectrograms can significantly affect the performance of the classifier and must be tackled using appropriate denoising algorithms. In recent years, deep learning algorithms have spawned many deep neural network-based denoising algorithms. For these methods, noise modelling is the most important part and is used to assist in training. In this paper, we decompose the problem and propose a novel denoising scheme: first, a Generative Adversarial Network (GAN) is used to learn the noise distribution and correlation from the real-world environment; then, a simulator is used to generate clean Micro-Doppler spectrograms; finally, the generated noise and clean simulation data are combined as the training data to train a Convolutional Neural Network (CNN) denoiser. In experiments, we qualitatively and quantitatively analyzed this procedure on both simulation and measurement data. Besides, the idea of learning from natural noise can be applied well to other existing frameworks and demonstrate greater performance than other noise models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
