Outlier Suppression via Non-Convex Robust PCA for Efficient Localization in Wireless Sensor Networks
Outlier Suppression via Non-Convex Robust PCA for Efficient Localization in Wireless Sensor Networks
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DOI：
10.1109/jsen.2017.2754502
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发表时间：
2017-11-01
影响因子：
4.3
通讯作者：
Li, Yujie
中科院分区：
文献类型：
--
作者：
Li, Xiang;Ding, Shuxue;Li, Yujie
针对无线传感器网络中离群点会显著降低定位精度的问题，提出了一种基于非凸稳健主成分分析的离群点抑制方法。通过引入几个非凸罚函数来逼近原稳健主成分分析问题中的秩函数和稀疏罚函数，建立了一个非凸目标函数，并利用增广拉格朗日乘子法和凸规划差分技术对其进行了有效求解。这个框架创造了通过标准主成分分析进行安全降维的机会，标准主成分分析在存在孤立点的情况下具有众所周知的脆弱特征。因此，通过稀疏编码获得的定位算法提供了低维计算效率的好处。在真实数据集上的无设备定位(DFL)实验表明，在存在离群点的情况下，标准主成分分析后的定位精度有所提高。引入了一种称为Wasserstein距离的统计距离来评价孤立点抑制的处理结果，说明了该方法能够识别和消除DFL问题中的孤立点。
Due to the fact that outliers can degrade localization accuracy significantly in wireless sensor networks, we propose an outlier suppression approach via non-convex robust principal component analysis (Robust PCA). By introducing several non-convex penalty functions to approximate both the rank function and the sparse penalty function in the original Robust PCA problem, we establish a non-convex objective function and solve it efficiently by the augmented Lagrangian multiplier method and difference of convex programming technique. This framework creates the opportunity to safely perform localization with dimension reduction by standard PCA, which has well-known fragile characteristics in the presence of outliers. Thus, the localization algorithms obtained by sparse coding provide the benefits of computational efficiency in low-dimension. The device-free localization (DFL) experiment on a real-world data set shows the improvement in localization accuracy after standard PCA in the presence of outliers. A statistical distance, called the Wasserstein distance, is introduced to evaluate the processed result of outlier suppression, illustrating that the proposed approach can identify and eliminate outliers in DFL problems.