项目名称: 多信道压缩采样实现多维射频层析成像的理论与方法研究
项目编号: No.61501218
项目类型: 青年科学基金项目
立项/批准年度: 2016
项目学科: 无线电电子学、电信技术
项目作者: 杨志勇
作者单位: 南昌航空大学
项目金额: 20万元
中文摘要: 射频层析成像是与视觉成像互补的一种新感知方法,因其鲜明的特点和优势,引起了许多研究者的关注,目前,国内在该方面的研究尚属起步阶段。本课题面对射频层析成像受环境变化、多径效应影响而成像精度不高的难点,拟研究多信道压缩采样实现多维射频层析成像的理论与方法,具体研究内容包括:1)射频层析成像传感模型;2)多信道无线链路RSS测量与预处理方法;3)压缩采样模型和多维图像重构算法。本项目从射频阴影衰落模型、多信道信号利用、多平面拟合三维立体成像三个方面提高成像精度,以压缩传感理论指导的闭环反馈测量方法保证成像效率。此方面的研究,既是对射频层析成像方法在多信道测量、多维成像方面的突破发展,也是对压缩感知理论应用的拓展,有重要的学术意义,在智能监控、公共安全、医疗监护、灾害搜救和危险环境勘测等领域有广泛的应用前景。
中文关键词: 射频层析成像;压缩感知;无线传感网网络;多信道;免持定位
英文摘要: Radio tomographic imaging (RTI) is a new sensing method, which can work in some situations where the traditional visual imaging can’t. Because of its distinctive features and advantages, RTI has attracted the attention of many researchers. Currently, the study in this field is at the beginning. Motivated by low imaging accuracy caused by multi-path effect and the changing environments, the proposal will study on multidimensional RTI and multi-channel sampling based on compressive sensing, and it will focus on, 1) radio tomography imaging sensing model; 2) methods of wireless links RSS measurements on multi-channel and pre-processing; 3) compressive sampling model and reconstruction algorithm of multidimensional RTI. With the support of the RF shadow fading model, RSS signals of multi-channel, three-dimensional imaging based on many plane imaging, the proposal will improve the imaging precision. The proposal will also ensure the imaging efficiency by using closed loop feedback measurements. While the results of the project will be a novel yet practical solution for three-dimensional RTI based on multi-channel measurements, it will also substantially contribute to the widespread development of the compressive sensing technologies. The studies of the project will have momentously practical and theoretical significances in several applications including intelligent monitoring, public security, medical monitoring, rescue missions, hazard environments surveying and monitoring so on.
英文关键词: Radio Tomograhpic Imaging;Compressive Sensing;Wireless Sensor Networks;Multi-Channel;Device-free Localization