Denoising autoencoders for signal processing applications have been shown to experience significant difficulty in learning to reconstruct radio frequency communication signals, particularly in the large sample regime. In communication systems, this challenge is primarily due to the need to reconstruct the modulated data stream which is generally highly stochastic in nature. In this work, we take advantage of this limitation by using the denoising autoencoder to instead remove interfering radio frequency communication signals while reconstructing highly structured FMCW radar signals. More specifically, in this work we show that a CNN-layer only autoencoder architecture can be utilized to improve the accuracy of a radar altimeter's ranging estimate even in severe interference environments consisting of a multitude of interference signals. This is demonstrated through comprehensive performance analysis of an end-to-end FMCW radar altimeter simulation with and without the convolutional layer-only autoencoder. The proposed approach significantly improves interference mitigation in the presence of both narrow-band tone interference as well as wideband QPSK interference in terms of range RMS error, number of false altitude reports, and the peak-to-sidelobe ratio of the resulting range profile. FMCW radar signals of up to 40,000 IQ samples can be reliably reconstructed.

翻译：信号处理应用中的去噪自编码器已被证明在学习重建射频通信信号方面存在显著困难，尤其是在大样本情况下。在通信系统中，这一挑战主要源于需要重建本质上通常具有高度随机性的调制数据流。在本工作中，我们利用这一局限性，转而使用去噪自编码器去除干扰性射频通信信号，同时重建高度结构化的FMCW雷达信号。更具体地说，本工作表明，即使在包含多种干扰信号的严重干扰环境中，仅采用CNN层的自编码器架构仍可用于提高雷达高度计测距估计的精度。这一结论通过对比分析端到端FMCW雷达高度计仿真系统在有无纯卷积层自编码器情况下的综合性能得到验证。所提方法在距离均方根误差、虚假高度报告数量以及生成距离剖面的峰值旁瓣比等指标上，对窄带单音干扰和宽带QPSK干扰均实现了显著的干扰抑制效果。该方法可可靠重建长达40,000个IQ样本的FMCW雷达信号。