People counting plays a crucial role in various sensing applications such as in smart cities and shopping malls. In this paper, we propose a data-driven solution that uses a low power ultra-wideband impulse (UWB) radar to count the number of random walking people in an indoor space. A pre-processing signal processing method is applied to clean clutter signals from UWB radar. Instead of the conventional counting methods, which manually extract features and learned from effective data patterns, we investigated deep convolutional neural networks (CNNs) that automatically learn from the data to count the number of people in an indoor space. The CNN model could accurately predict up to 97% accuracy for up to 10 people random walking in an area of 5 5 m. The different settings of the CNN models, such as the data input window size, and kernel size in each layer, will be investigated.

人数统计在智能城市和购物中心等各种传感应用中起着至关重要的作用。在本文中，我们提出了一种数据驱动的解决方案，该解决方案使用低功率超宽带脉冲 (UWB) 雷达来计算室内空间中随机行走的人数。一种预处理信号处理方法被应用于清除来自超宽带雷达的杂波信号。与手动提取特征并从有效数据模式中学习的传统计数方法不同，我们研究了深度卷积神经网络 (CNN)，该网络可自动从数据中学习以计算室内空间的人数。CNN 模型可以准确预测多达 10 人在 5 5 m 区域内随机行走的准确率高达 97%。CNN 模型的不同设置，例如数据输入窗口大小，