Zhang Jie, Xiao Wendong, Zhang Sen, Huang Shoudong
School of Automation & Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Centre for Autonomous Systems, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia.
Sensors (Basel). 2017 Apr 17;17(4):879. doi: 10.3390/s17040879.
Device-free localization (DFL) is becoming one of the new technologies in wireless localization field, due to its advantage that the target to be localized does not need to be attached to any electronic device. In the radio-frequency (RF) DFL system, radio transmitters (RTs) and radio receivers (RXs) are used to sense the target collaboratively, and the location of the target can be estimated by fusing the changes of the received signal strength (RSS) measurements associated with the wireless links. In this paper, we will propose an extreme learning machine (ELM) approach for DFL, to improve the efficiency and the accuracy of the localization algorithm. Different from the conventional machine learning approaches for wireless localization, in which the above differential RSS measurements are trivially used as the only input features, we introduce the parameterized geometrical representation for an affected link, which consists of its geometrical intercepts and differential RSS measurement. Parameterized geometrical feature extraction (PGFE) is performed for the affected links and the features are used as the inputs of ELM. The proposed PGFE-ELM for DFL is trained in the offline phase and performed for real-time localization in the online phase, where the estimated location of the target is obtained through the created ELM. PGFE-ELM has the advantages that the affected links used by ELM in the online phase can be different from those used for training in the offline phase, and can be more robust to deal with the uncertain combination of the detectable wireless links. Experimental results show that the proposed PGFE-ELM can improve the localization accuracy and learning speed significantly compared with a number of the existing machine learning and DFL approaches, including the weighted K-nearest neighbor (WKNN), support vector machine (SVM), back propagation neural network (BPNN), as well as the well-known radio tomographic imaging (RTI) DFL approach.
无设备定位（DFL）正成为无线定位领域的新技术之一，这是因为其具有待定位目标无需附着任何电子设备的优势。在射频（RF）DFL系统中，无线发射机（RTs）和无线接收机（RXs）协同用于感知目标，并且可以通过融合与无线链路相关的接收信号强度（RSS）测量值的变化来估计目标的位置。在本文中，我们将提出一种用于DFL的极限学习机（ELM）方法，以提高定位算法的效率和准确性。与用于无线定位的传统机器学习方法不同，在传统方法中上述差分RSS测量值被简单地用作唯一输入特征，我们引入了受影响链路的参数化几何表示，它由其几何截距和差分RSS测量值组成。对受影响的链路执行参数化几何特征提取（PGFE），并将这些特征用作ELM的输入。所提出的用于DFL的PGFE-ELM在离线阶段进行训练，并在在线阶段用于实时定位，在在线阶段通过创建的ELM获得目标的估计位置。PGFE-ELM的优点在于，ELM在在线阶段使用的受影响链路可以与离线阶段用于训练的链路不同，并且在处理可检测无线链路的不确定组合时可以更加稳健。实验结果表明，与许多现有的机器学习和DFL方法相比，所提出的PGFE-ELM可以显著提高定位精度和学习速度，这些方法包括加权K近邻（WKNN）、支持向量机（SVM）、反向传播神经网络（BPNN）以及著名的无线电层析成像（RTI）DFL方法。
