基于纯CNN层去噪自编码器架构的飞机雷达高度计干扰抑制
Aircraft Radar Altimeter Interference Mitigation Through a CNN-Layer Only Denoising Autoencoder Architecture
AI总结：
针对射频通信信号干扰下FMCW雷达高度计测距精度下降的问题，提出纯CNN层去噪自编码器架构，可在强干扰环境下可靠重建雷达信号，显著提升干扰抑制性能与测距精度。
摘要：
用于信号处理的去噪自编码器已被证实在学习重建射频通信信号时存在显著困难，尤其是在大样本场景下。在通信系统中，这一挑战主要源于需要重建本质上高度随机的调制数据流。在本研究中，我们利用这一局限性，转而使用去噪自编码器在重建高度结构化的FMCW雷达信号的同时，去除干扰性射频通信信号。更具体地说，本研究表明，纯CNN层自编码器架构可用于提升雷达高度计的测距估计精度，即便在包含大量干扰信号的强干扰环境中依然有效。我们通过对带与不带纯卷积层自编码器的端到端FMCW雷达高度计仿真进行全面性能分析，验证了这一点。所提方法在距离均方根误差、错误高度报告数量以及所得距离像的峰旁瓣比方面，显著提升了对窄带单音干扰和宽带QPSK干扰的抑制效果。多达40000个IQ样本的FMCW雷达信号可被可靠重建。
英文摘要：
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.
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