Convolutional neural networks have often been proposed for processing radar
Micro-Doppler signatures, most commonly with the goal of classifying the
signals. The majority of works tend to disregard phase information from the
complex time-frequency representation. Here, the utility of the phase
information, as well as the optimal format of the Doppler-time input for a
convolutional neural network, is analysed. It is found that the performance
achieved by convolutional neural network classifiers is heavily influenced by
the type of input representation, even across formats with equivalent
information. Furthermore, it is demonstrated that the phase component of the
Doppler-time representation contains rich information useful for classification
and that unwrapping the phase in the temporal dimension can improve the results
compared to a magnitude-only solution, improving accuracy from 0.920 to 0.938
on the tested human activity dataset. Further improvement of 0.947 is achieved
by training a linear classifier on embeddings from multiple-formats.

对于处理雷达微多普勒迹象，研究中分析了卷积神经网络的相位信息的实用性以及多普勒 - 时间输入的最佳格式。研究发现，卷积神经网络分类器的性能受到输入表示类型的影响，即使在具有等效信息的不同格式之间也是如此。此外，研究证明了多普勒 - 时间表示的相位分量包含用于分类的丰富信息，并且在时间维度上进行相位解包能够改善结果，将在测试的人体活动数据集上的准确性从 0.920 提高到 0.938。通过在多种格式上嵌入进行线性分类器的训练，进一步改进，达到 0.947 的准确性。

卷积神经网络用于雷达微多普勒特征处理的输入格式

On Input Formats for Radar Micro-Doppler Signature Processing by  Convolutional Neural Networks

Deep learning has become an area of interest in most scientific areas,
including physical sciences. Modern networks apply real-valued transformations
on the data. Particularly, convolutions in convolutional neural networks
discard phase information entirely. Many deterministic signals, such as seismic
data or electrical signals, contain significant information in the phase of the
signal. We explore complex-valued deep convolutional networks to leverage
non-linear feature maps. Seismic data commonly has a lowcut filter applied, to
attenuate noise from ocean waves and similar long wavelength contributions.
Discarding the phase information leads to low-frequency aliasing analogous to
the Nyquist-Shannon theorem for high frequencies. In non-stationary data, the
phase content can stabilize training and improve the generalizability of neural
networks. While it has been shown that phase content can be restored in deep
neural networks, we show how including phase information in feature maps
improves both training and inference from deterministic physical data.
Furthermore, we show that the reduction of parameters in a complex network
outperforms larger real-valued networks.

本文研究探讨了使用复数和包含相位信息的卷积神经网络对具有相位信息的信号（例如地震数据或电气信号）进行非线性特征提取，实验表明，这种方法能提高网络的泛化性能和准确率。

复值神经网络在非平稳物理数据的机器学习中的应用

Complex-valued neural networks for machine learning on non-stationary  physical data

The "cocktail party" problem of fully separating multiple sources from a
single channel audio waveform remains unsolved. Current biological
understanding of neural encoding suggests that phase information is preserved
and utilized at every stage of the auditory pathway. However, current
computational approaches primarily discard phase information in order to mask
amplitude spectrograms of sound. In this paper, we seek to address whether
preserving phase information in spectral representations of sound provides
better results in monaural separation of vocals from a musical track by using a
neurally plausible sparse generative model. Our results demonstrate that
preserving phase information reduces artifacts in the separated tracks, as
quantified by the signal to artifact ratio (GSAR). Furthermore, our proposed
method achieves state-of-the-art performance for source separation, as
quantified by a mean signal to interference ratio (GSIR) of 19.46.

研究阐述了利用神经可行性稀疏生成模型中保留声音频谱的相位信息，可以更好地分离音频中的人声与音乐，减少了分离出的音频中的伪声，同时取得了最先进的状态分离性能。