Ultra-wideband Through-wall Radar Research Articles

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.

Estimation and Mitigation of Time-Variant RFI Based on Iterative Dual Sparse Recovery in Ultra-Wide Band Through-Wall Radar

This paper focuses on the time-variant radio frequency interference (RFI) issue that ultra-wide band (UWB) through-wall radar (TWR) is faced with, and presents an iterative dual sparse recovery (IDSR) framework to combat it. The framework consists of two stages: 1) RFI estimation and detection and 2) IDSR of scattered echoes from objects and RFI signals. In the first stage, an overlapped short time Fourier transform is employed to construct and update the discrete frequency Doppler spectrum (DFDS). Then, a minimum statistic operation is conducted on the DFDS to estimate RFI signals, followed by detection via a 1-D cell-averaging constant false alarm rate detector to determinate whether RFI signals exist or not. In the second stage, a dual sparse model of the collected signals is set up, based on the fast-time frequency sparsity of RFI signals because of their narrow bands and the Doppler frequency sparsity of scattered echoes from objects because of their limited moving velocities. The alternating direction method of multipliers (ADMM) is introduced to iteratively and alternately recover RFI signals and scattered echoes from objects. Specifically, the iterative hard thresholding (IHT) method is used to complete the two sparse recovery operations. The involved two sparse dictionaries are simple and only made up of inverse discrete Fourier transform basis and independent of the received signals. The IDSR framework is able to relieve the influences of RFI signals and scattered echoes from objects on each other, and thus can reconstruct the two type signals to the maximum limit. Field experiments using a UWB TWR were carried out to verify the proposed method.

Array-Based Ultrawideband through-Wall Radar: Prediction and Assessment of Real Radar Abilities

This paper deals with a new through-the-wall (TTW) radar demonstrator for the detection and the localisation of people in a room (in a noncooperative way) with the radar situated outside but in the vicinity of the first wall. After modelling the propagation through various walls and quantifying the backscattering by the human body, an analysis of the technical considerations which aims at defining the radar design is presented. Finally, an ultrawideband (UWB) frequency modulated continuous wave (FMCW) radar is proposed, designed, and implemented. Some representative trials show that this radar is able to localise and track moving people behind a wall in real time.

Target Localization and Identification Algorithm for Ultra Wideband Through-wall Radar

穿墙目标探测是超宽带脉冲雷达在反恐斗争、灾难救援、城市巷战等方面的一个重要应用。SEABED算法是基于超宽带脉冲雷达的一种新型目标定位识别算法，但其不适用于对障碍物背后的目标进行定位的情况。该文在SEABED算法的基础上，分析SEABED算法在穿墙探测中的不足，提出一种新的定位算法使得SEABED算法能够对目标进行定位识别。该算法假设在近场条件下，利用回波时延差的关系曲线求得入射角，从而结合几何关系消除墙体对传播时延和传播路径的影响，将SEABED算法应用于穿墙成像应用中。仿真结果表明，该算法能够弥补墙体对SEABED算法的影响，精确度高，目标定位识别效果好，达到穿墙探测的目的。