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Radio Tomographic Imaging with Feedback-Based Sparse Bayesian Learning

机译：射频断层摄影成像与基于反馈的稀疏贝叶斯学习

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摘要

Radio tomographic imaging (RTI) provides an efficient method to realize device-free localization (DFL) which does not require the target to carry any tags or electronic devices. By the measurement of received signal strength (RSS) between node pairs in a wireless sensor network, the attenuation image caused by the target can be reconstructed. Subsequently, the target location can be extracted from the attenuation image. Sparse Bayesian learning (SBL) can be employed for reconstruction because of the sparseness of the attenuation image. However, the fast SBL degrades in reconstruction performances due to the inaccurate estimation on the noise hyper-parameters. To address this, this paper exploits a feedback-based fast SBL framework both for homogeneous-noise and heterogeneous-noise cases. Theoretical modeling and Bayesian inference procedure are given for this feedback-based framework. Finally, RTI experimental results from three different scenarios demonstrate the effectiveness of the proposed scheme.
机译：无线电断层成像（RTI）提供了一种实现无设备定位（DFL）的有效方法，其不需要目标携带任何标签或电子设备。通过在无线传感器网络中的节点对之间的接收信号强度（RSS）的测量，可以重建由目标引起的衰减图像。随后，可以从衰减图像中提取目标位置。由于衰减图像的稀疏性，可以使用稀疏贝叶斯学习（SBL）来重建。然而，由于噪声超参数的估计不准确，快速SBL降低了重建性能。为了解决此问题，本文利用基于反馈的快速SBL框架，用于均匀噪声和异质噪声情况。基于反馈的框架给出了理论建模和贝叶斯推理程序。最后，三种不同情景的RTI实验结果证明了所提出的方案的有效性。

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