Dataset of human motion status using IR-UWB through-wall radar

Ultra-wideband(UWB)through-wall radar has a wide range of applications in non-contact human information detection and monitoring.With the integration of machine learning technology,its potential prospects include the physiological monitoring of patients in the hospital environment and the daily monitoring at home.Although many target detection methods of UWB through-wall radar based on machine learning have been proposed,there is a lack of an opensource dataset to evaluate the performance of the algorithm.This published dataset is measured by impulse radio UWB(IR-UWB)through-wall radar system.Three test subjects are measured in different environments and several defined motion status.Using the presented dataset,we propose a human-motion-status recognition method using a convolutional neural network(CNN),and the detailed dataset partition method and the recognition process flow are given.On the well-trained network,the recognition accuracy of testing data for three kinds of motion status is higher than 99.7%.The dataset presented in this paper considers a simple environment.Therefore,we call on all organizations in the UWB radar field to cooperate to build opensource datasets to further promote the development of UWB through-wall radar.

Algorithm for Gesture Recognition Using an IR-UWB Radar Sensor

This paper introduces a human gesture recognition algorithm using an impulse radio ultra-wide- band (IR-UWB) radar sensor. Human gesture recognition has been one of the hottest research topics for quite a long time. Many gesture recognition algorithms or systems using other sensors have been proposed such as using cameras, RFID tags and so on. Among which gesture recognition systems using cameras have been extensively studied in past years and widely used in practical. While it might show some deficiencies in some cases. For example, the users might not like to be filmed by cameras considering their privacies. Besides, it might not work well in very dark environments. While RFID tags could be inconvenient to many people and are likely to be lost. Our gesture recognition algorithm uses IR-UWB radar sensor which has pretty high resolution in ranging and adjustable gesture recognition range, meanwhile, does not have problems in privacy issues or darkness. In this paper, the gesture recognition algorithm is based on the moving direction and distance change of the human hand and the change of the frontal surface area of hand towards radar sensor. By combining these changes while doing gestures, the algorithm may recognize basically 6 kinds of hand gestures. The experimental results show that these gestures are of quite good performance. The performance analysis from experiments is also given.

Overview of Ultra-Wideband Transceivers——System Architectures and Applications

The Ultra-WideBand(UWB) technique, which offers good energy efficiency, flexible data rate, and high ranging accuracy, has recently been recognized as a revived wireless technology for short distance communication.This paper presents a brief overview of two UWB techniques, covering Impulse-Radio UWB(IR-UWB) and FrequencyModulation UWB(FM-UWB) methods. The link margin enhancement technique, Very-WideBand(VWB), and power consumption reducing technique, chirp UWB, are also introduced. Then, several potential applications of IR-UWB with transceiver architectures are addressed, including high data rate proximity communication and secure wireless connectivity. With fine-ranging and energy-efficient communication features, the UWB wireless technology is highly promising for secure mobile Internet of Things(IoT) applications.

脉冲式超宽带生命探测雷达时间抖动抑制

脉冲式超宽带生命探测雷达的时间抖动将静态杂波变为无规律扰动的动态杂波,导致对强静态杂波进行传统去直流处理后仍存在较强残余,容易形成虚警并造成误判。该文首先分析了时间抖动对脉冲式超宽带生命探测雷达回波的影响,然后指出耦合回波间的相对时延能够反映雷达系统的时间抖动,在此基础上提出了一种基于耦合回波的时间抖动抑制方法。实测数据结果证明该文所提方法能够有效抑制强静态杂波的残余,进一步提高生命探测性能。