Radio Tomographic Imaging with Feedback-based Bayesian Compressed Sensing
Radio tomographic imaging(RTI)provides a powerful mean for device-free localization(DFL),which utilizes the received signal strength(RSS)to reconstruct the attenuation image caused by the target.The reconstruction problem can be modeled as a Bayesian compressed sensing(BCS)problem.However,the fast BCS algorithm degrades in reconstruction performances due to the inaccurate estimation of the noise hyper-parameters.To address this,this paper presents a framework of feedback-based fast BCS both for homogeneous and heterogeneous cases.Theoretical modeling and Bayesian inference procedure are given for this feedback-based framework.Finally,RTI experimental results from three scenarios demonstrate the effectiveness of the proposed scheme.
RTI DFL feedback-based BCS homogeneous heterogeneous
Zhen Wang Xuemei Guo Guoli Wang
Sun Yat-sen University,Guangzhou 510006,China;Guangzhou college of South China University of Technol Sun Yat-sen University,Guangzhou 510006,China
国内会议
厦门
英文
200-206
2017-11-17（万方平台首次上网日期，不代表论文的发表时间）