Deep learning with its rapid development and advancement has achieved unparalleled performance in many areas like computer vision as well as cognitive radio and signal recognition. However, the performance of most deep neural networks would suffer from degradation in the data mismatch scenario, e.g., the test dataset has a related but nonidentical distribution with the training dataset. Considering the noise corruption, a classifier’s accuracy might drop sharply when it is tested on a dataset with much lower signal-to-noise ratio compared to its training dataset. To address this dilemma, in this work, we propose an efficient denoising and classification network (DNCNet) for radar signals. The DNCNet consists of denoising and classification subnetworks. First, a radar signal detection and synthetic mechanism is designed to generate pairwise clean data and noisy data for the DNCNet to train its denoising subnetwork. Then, a two-phase training procedure is proposed to train the denoising subnetwork in the first phase and strengthen the mapping between the denoising results and perceptual representation in the second. Experiments on synthetic and benchmark datasets validate the excellent performance of the proposed DNCNet against state-of-the-art methods in terms of both signal restoration quality and classification accuracy.

中文翻译：

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DNCNet：深度雷达信号去噪和识别


深度学习的快速发展和进步，在计算机视觉、认知无线电和信号识别等许多领域取得了无与伦比的性能。然而，大多数深度神经网络的性能在数据不匹配的情况下会受到影响，例如，测试数据集与训练数据集具有相关但不相同的分布。考虑到噪声损坏，当在信噪比远低于训练数据集的数据集上进行测试时，分类器的准确性可能会急剧下降。为了解决这个困境，在这项工作中，我们提出了一种针对雷达信号的高效去噪和分类网络（DNCNet）。 DNCNet 由去噪和分类子网络组成。首先，设计了雷达信号检测和合成机制，为 DNCNet 生成成对的干净数据和噪声数据来训练其去噪子网络。然后，提出了一个两阶段训练过程，在第一阶段训练去噪子网络，并在第二阶段加强去噪结果和感知表示之间的映射。对合成数据集和基准数据集的实验验证了所提出的 DNCNet 在信号恢复质量和分类精度方面相对于最先进的方法的优异性能。