基于聚集Bayes压缩感知的射频层析成像  

Radio tomographic imaging based on cluster Bayesian compressive sensing

作　　者：谭家驹 秦乐 赵新[1,2] 郭雪梅[5,4] 王国利[5,4] Jiaju TAN1,2, Le QIN3,5, Xin ZHAO1,2, Xuemei GUO4,5, Guoli WANG4,5(1. Institute of Robotics and Automatic Information System, Nankai University, Tianjin 300350, China; 2. Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300350, China; 3. School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China; 4. School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China; 5. Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, Sun Yat-Sen Uni versity, Guangzhou 510006, Chin)

机构地区：[1]南开大学机器人与信息自动化研究所,天津300350 [2]南开大学天津市智能机器人技术重点实验室,天津300350 [3]中山大学电子与信息工程学院,广州510006 [4]中山大学机器智能与先进计算教育部重点实验室,广州510006 [5]中山大学数据科学与计算机学院,广州510006

出　　处：《中国科学：信息科学》2018年第7期903-918,共16页Scientia Sinica(Informationis)

基　　金：国家自然科学基金(批准号:61772574;61375080);大型科学仪器设备共享专项(批准号:2015B030304001);广东省自然科学基金重点项目(批准号:2015A030311049)资助

摘　　要：为有效对抗窄带射频层析成像测量过程中所存在的多径干扰,实现对阴影衰落的有效估计,本文结合结构化聚集Bayes压缩感知理论,依据阴影衰落所具有的稀疏性和空间区域聚集性特征,构造阴影衰落分布的聚集稀疏先验模型,建立射频层析成像的结构化稀疏Bayes学习机制,增强射频链路对阴影衰落与其他多径衰落的辨别能力,有效抑制伪影的产生,提升射频层析成像技术对观测目标的识别性能,更好地服务于无源位置感知的实际应用.In narrowband radio tomographic imaging, the crucial challenge is to detect multipath interference effectively and obtain a better estimate of shallow fading. Based on structural cluster Bayesian compressive-sensing theory, an analysis is presented of the possible shallow fading status, and more spatial distribution information of the shallow fading is explored. As a result, a more accurate prior model combining the sparsity and cluster property of shallow fading and a better computational imaging mechanism is proposed. The experimental results show that this cluster sparsity Bayesian compressive-sensing model restrains the artifacts in the image by improving the link discrimination ability between shallow fading and multipath interference so that better recovered images can be obtained, and device-free localization performance is improved.

关 键 词：窄带射频传感网络 无源位置感知 射频层析成像 多径干扰 Bayes压缩感知 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]