Advanced support vector machines for 802.11 indoor location
Advanced support vector machines for 802.11 indoor location
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DOI：
10.1016/j.sigpro.2012.01.026
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发表时间：
2012-09-01
影响因子：
4.4
通讯作者：
Caamano, Antonio J.
中科院分区：
文献类型：
--
作者：
Figuera, Carlos;Luis Rojo-Alvarez, Jose;Caamano, Antonio J.
由于普适计算服务的激增，近年来，在室内场景中定位设备受到了特别关注。多种算法基于对接收信号强度的 Wi-Fi 测量，并使用先前在已知位置的测量来估计该信号与位置之间的关系。这个问题自然很适合神经网络或支持向量机 (SVM) 等学习算法。然而，现有的机器学习技术并没有明显优于其他更简单的技术，例如 k-nn。这主要是因为这些解决方案不包含重要的先验信息。在本文中，我们提出了一种增强这些算法的技术，通过在学习机中包含某些先验信息、使用训练集的光谱信息以及复杂的输出来利用位置二维中的交叉信息。具体来说，我们修改 SVM 算法以获得三种结合此信息的高级方法：一种使用自相关核，另一种使用复杂输出，第三种将两者结合起来。将这些算法与 k-nn 和具有高斯核的 SVM 进行比较，表明包含先验信息可以提高定位性能。 (C) 2012 Elsevier B.V. 保留所有权利。
Due to the proliferation of ubiquitous computing services, locating a device in indoor scenarios has received special attention during recent years. A variety of algorithms are based on Wi-Fi measurements of the received signal strength and estimate the relation between this one and position using previous measurements at known locations. This problem naturally fits in well with learning algorithms such as neural networks, or support vector machines (SVM). However, existing machine learning techniques do not significantly outperform other simpler techniques, such as k-nn. This is mainly due to the fact that these solutions do not include significant a priori information. In this paper, we propose a technique to enhance these algorithms by including certain a priori information within the learning machine, using the spectral information of the training set, and a complex output to take advantage of the cross information in the two dimensions of the location. Specifically, we modify a SVM algorithm to obtain three advanced methods incorporating this information: one using an autocorrelation kernel, another using a complex output, and a third one combining both. These algorithms are compared to the k-nn and an SVM with Gaussian kernel, showing that including the a priori information improves the location performance. (C) 2012 Elsevier B.V. All rights reserved.