用深度学习和矩阵补全修复含异常值的物联网定位距离矩阵
Deep Learning and Matrix Completion-aided IoT Network Localization in the Outlier Scenarios
- 将距离矩阵建模为传感器坐标矩阵的函数，约束解空间为欧式距离矩阵
- 通过交替优化坐标、距离和异常值矩阵，实现高精度定位恢复
- 适合在存在异常数据的物联网场景中使用，鲁棒性强
本文提出一种结合深度学习与矩阵补全的方法，用于恢复受异常值污染的物联网网络定位中的欧氏距离矩阵 D。不同于传统方法在整个矩阵空间中搜索解，该方法将搜索范围限制在欧氏距离矩阵集合内。具体地，将 D 表示为传感器坐标矩阵 X 的函数，该函数天然满足 D 的唯一性质，并联合使用深度神经网络恢复 D 与 X。为有效处理异常值，将其建模为稀疏矩阵 L 并加入正则项至优化问题，通过交替更新 X、D 与 L 求解。数值实验表明，该方法在存在异常值的情况下仍能准确恢复传感器位置信息。
原文摘要 · Abstract (English)
In this paper, we propose a deep learning and matrix completion aided approach for recovering an outlier contaminated Euclidean distance matrix D in IoT network localization. Unlike conventional localization techniques that search the solution over a whole set of matrices, the proposed technique restricts the search to the set of Euclidean distance matrices. Specifically, we express D as a function of the sensor coordinate matrix X that inherently satisfies the unique properties of D, and then jointly recover D and X using a deep neural network. To handle outliers effectively, we model them as a sparse matrix L and add a regularization term of L into the optimization problem. We then solve the problem by alternately updating X, D, and L. Numerical experiments demonstrate that the proposed technique can recover the location information of sensors accurately even in the presence of outliers.
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