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.
作者
谭家驹
秦乐
赵新
郭雪梅
王国利
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)
出处
《中国科学：信息科学》
CSCD
北大核心
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
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
narrowband radio frequency sensor networks
device-free localization
radio tomographic imaging
multipath interference
Bayesian compressive sensing
