Radio tomographic imaging (RTI) is an emerging technology for localization of physical objects in a geographical area covered by wireless networks. With attenuation measurements collected at spatially distributed sensors, RTI capitalizes on spatial loss fields (SLFs) measuring the absorption of radio frequency waves at spatial locations along the propagation path. These SLFs can be utilized for interference management in wireless communication networks, environmental monitoring, and survivor localization after natural disasters such as earthquakes. Key to the success of RTI is to accurately model shadowing as the weighted line integral of the SLF. To learn the SLF exhibiting statistical heterogeneity induced by spatially diverse environments, the present work develops a Bayesian framework entailing a piecewise homogeneous SLF with an underlying hidden Markov random field model. Utilizing variational Bayes techniques, the novel approach yields efficient field estimators at affordable complexity. A data-adaptive sensor selection strategy is also introduced to collect informative measurements for effective reconstruction of the SLF. Numerical tests using synthetic and real datasets demonstrate the capabilities of the proposed approach to radio tomography and channel-gain estimation.

中文翻译：

---

自适应无线电断层扫描的变分贝叶斯方法

无线电断层成像 (RTI) 是一种新兴技术，用于在无线网络覆盖的地理区域中定位物理对象。通过在空间分布的传感器上收集衰减测量值，RTI 利用空间损耗场 (SLF) 测量沿传播路径的空间位置处的射频波吸收。这些 SLF 可用于无线通信网络中的干扰管理、环境监测和地震等自然灾害后的幸存者定位。RTI 成功的关键是将阴影准确建模为 SLF 的加权线积分。为了学习 SLF 表现出由空间多样化环境引起的统计异质性，目前的工作开发了一个贝叶斯框架，它需要一个分段齐次 SLF 和一个潜在的隐马尔可夫随机场模型。利用变分贝叶斯技术，这种新颖的方法以可承受的复杂性产生有效的场估计器。还引入了数据自适应传感器选择策略来收集用于有效重建 SLF 的信息测量值。使用合成和真实数据集的数值测试证明了所提出的无线电断层扫描和信道增益估计方法的能力。