Blind Radio Tomography
Blind Radio Tomography
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DOI：
10.1109/tsp.2018.2799169
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发表时间：
2018-04
影响因子：
5.4
通讯作者：
Daniel Romero;Donghoon Lee;G. Giannakis
中科院分区：
文献类型：
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作者：
Daniel Romero;Donghoon Lee;G. Giannakis
从空间分布的传感器网络收集的衰减测量，无线电断层扫描构造空间损耗场（SLF），量化每个位置的射频波的吸收。这些SLF可以用于干扰预测（可能是认知）无线通信网络，环境监测或入侵检测的监视应用，通过墙成像，地震或火灾后的幸存者定位等无线电断层扫描的基石是模型衰减的二维积分的SLF的兴趣缩放的权重函数。不幸的是，现有的方法（i）依赖于启发式假设来选择权重函数，以及（ii）限于对传播介质中的变化进行成像，或者它们需要在自由空间中进行测量的单独校准步骤。本文的第一个主要贡献是（i）通过盲无线电层析成像方法，该方法从衰减测量中学习SLF以及上述权重函数。通过利用当代基于内核的学习工具以及利用先验知识的各种形式的正则化来解决这个具有挑战性的问题。第二个贡献地址（ii）通过一种新的校准技术，能够成像静态结构，而无需单独的校准步骤。数值试验与真实的和合成的测量验证了所提出的算法的有效性。
From the attenuation measurements collected by a network of spatially distributed sensors, radio tomography constructs spatial loss fields (SLFs) that quantify absorption of radiofrequency waves at each location. These SLFs can be used for interference prediction in (possibly cognitive) wireless communication networks, for environmental monitoring or intrusion detection in surveillance applications, for through-the-wall imaging, for survivor localization after earthquakes or fires, etc. The cornerstone of radio tomography is to model attenuation as the bidimensional integral of the SLF of interest scaled by a weight function. Unfortunately, existing approaches (i) rely on heuristic assumptions to select the weight function and (ii) are limited to imaging changes in the propagation medium or they require a separate calibration step with measurements in free space. The first major contribution in this paper addresses (i) by means of a blind radio tomographic approach that learns the SLF together with the aforementioned weight function from the attenuation measurements. This challenging problem is tackled by capitalizing on contemporary kernel-based learning tools together with various forms of regularization that leverage prior knowledge. The second contribution addresses (ii) by means of a novel calibration technique capable of imaging static structures without separate calibration steps. Numerical tests with real and synthetic measurements validate the efficacy of the proposed algorithms.