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首页> 外文期刊>IEEE sensors journal >Data-Driven Radar Processing Using a Parametric Convolutional Neural Network for Human Activity Classification
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Data-Driven Radar Processing Using a Parametric Convolutional Neural Network for Human Activity Classification

机译：数据驱动雷达处理使用参数卷积神经网络进行人类活动分类

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摘要

The paper proposes a data-driven pre-processing optimization for radar data using a parametric convolutional neural network. The proposed method is applied on human activity classification as a use case. Present radar-based activity recognition system exploit micro-Doppler signature by generating Doppler spectrograms or a temporal series of range-Doppler maps, followed by deep neural networks or machine learning approaches for classification. Those radar data representations are typically generated on the basis of short-time Fourier transformations. A Fourier transformation equally resolves the frequency space, which may be sub-optimal in some applications. Although deep convolutional neural networks (DCNN) have been shown to implicitly learn features from raw sensor data in other fields, such as speech recognition, yet, for the case of radar-based DCNNs, pre-processing is required to develop a scalable and robust classification or regression application. In this paper, we propose a parametric convolutional neural network that mimics the radar pre-processing across fast-time and slow-time radar data through 2D sinc filter or 2D wavelet filter kernels to extract features for classification of various human activities. During training only the filter parameters of the 2D sinc filters or 2D wavelets are learned, leading to optimized feature representation for the classification task. It is demonstrated that our proposed solution shows improved results compared to equivalent DCNN architectures that rely on Doppler spectrograms or radar data cubes as input data.
机译：本文提出了使用参数卷积神经网络的雷达数据的数据驱动预处理优化。该方法应用于人类活动分类作为用例。目前基于雷达的活动识别系统通过生成多普勒谱图或颞级系列 - 多普勒地图来利用微多普勒签名，其次是深度神经网络或机器学习方法进行分类。这些雷达数据表示通常在短时傅里叶变换的基础上生成。傅里叶变换同样地解析频率空间，在某些应用中可能是子最佳的。虽然已经显示了深度卷积神经网络（DCNN），但已经隐含地从原始传感器数据中隐式学习特征，例如语音识别，对于基于雷达的DCNN的情况，需要预处理来开发可扩展且鲁棒分类或回归申请。在本文中，我们提出了一种参数卷积神经网络，通过2D SINC滤波器或2D小波滤波器核来模拟快速时间和慢速雷达数据的雷达预处理，以提取各种人类活动分类的特征。在训练期间，仅学习了2D SIST滤波器或2D小波的滤波器参数，从而导致分类任务的优化特征表示。据证明，与依赖于多普勒谱图或雷达数据立方体作为输入数据的等效DCNN架构，我们提出的解决方案显示了改进的结果。

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