Classification accuracy with various distances.

Nowadays, classifying human activities is applied in many essential fields, such as healthcare, security monitoring, and search and rescue missions. Radar sensor-based human activity classification is regarded as a superior approach in comparison to other techniques, such as visual perception-based methodologies and wearable gadgets. However, noise usually exists throughout the process of extracting raw radar signals, decreasing the quality and reliability of the extracted features. This paper presents a novel method for removing white Gaussian noise from raw radar signals using a denoising algorithm before classifying human activities using a deep convolutional neural network (DCNN). Specifically, the denoising algorithm is used as a preprocessing step to remove white Gaussian noise from the input raw radar signal. After that, a lightweight Cross-Residual Convolutional Neural Network (CRCNN) with adaptable cross-residual connections is suggested for classification. The analysis results show that the denoising algorithm with a range-bin interval of 3 and a cut-threshold value of 3 achieves the best denoising effect. When the denoising algorithm was applied to the dataset, CRCNN improved the right classification rate by up to 10% compared to the recognition results achieved with the original noise-added dataset. Additionally, a comparison of the CRCNN with the denoising algorithm solution with six cutting-edge DCNNs was conducted. The experimental results reveal that the proposed model greatly outperforms the others.

当前，人类活动分类技术已广泛应用于医疗健康、安全监控、搜救任务等诸多关键领域。相较于基于视觉感知的方法与可穿戴设备等其他技术方案，基于雷达传感器的人类活动分类被视作更具优势的技术途径。然而，在原始雷达信号的提取过程中通常会引入噪声，这会降低所提取特征的质量与可靠性。本文提出一种新颖的处理方案：在利用深度卷积神经网络（Deep Convolutional Neural Network, DCNN）开展人类活动分类前，先通过去噪算法去除原始雷达信号中的高斯白噪声。具体而言，该去噪算法将作为预处理步骤，对输入的原始雷达信号中的高斯白噪声进行滤除；在此基础上，本文提出一种具备自适应跨残差连接的轻量级跨残差卷积神经网络（Cross-Residual Convolutional Neural Network, CRCNN）用于人类活动分类任务。分析结果表明，当距离单元间隔设为3、截断阈值设为3时，该去噪算法可取得最优的去噪效果。将该去噪算法应用于数据集后，相较于原始含噪数据集的识别结果，CRCNN的正确分类率最高可提升10%。此外，本文将该搭载去噪算法的CRCNN方案与六种前沿DCNN模型开展对比实验，实验结果表明，所提模型的性能显著优于其余对比模型。