Device-Free Localization via Dictionary Learning With Difference of Convex Programming
Device-Free Localization via Dictionary Learning With Difference of Convex Programming
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DOI：
10.1109/jsen.2017.2730226
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发表时间：
2017-09
影响因子：
4.3
通讯作者：
Shuxue Ding;Zhenni Li;Benying Tan
中科院分区：
文献类型：
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作者：
Shuxue Ding;Zhenni Li;Benying Tan
在本文中，我们考虑一种方法来解决无设备定位（DFL）的问题，能够通过无线网络检测空间障碍。提出了一种基于学习数据的目标定位字典学习方法-差分凸规划（DC）和DC算法。通过测量指示障碍物位置的无线链路的接收信号强度的变化，可以通过学习的字典来估计监视区域中的物理目标。我们表明，DFL问题可以有效地表示为非凸优化问题。我们采用了一个罚函数称为极小极大凹罚，它具有良好的性质，在寻求稀疏性，并解决了非凸优化问题使用DC规划。此外，在路径跟踪任务中实现的定位精度进一步提高所提出的跟踪邻域规则。该规则提供了一个解决方案，以提高定位精度下的时变条件下产生的采样信道的传感器网络在噪声条件下。所提出的方法在真实世界的数据集上进行了验证，并有可能在DFL应用中灵活地采用。
In this paper, we consider a method to solve the device-free localization (DFL) problem that is able to detect spatial obstruction via wireless network. A dictionary learning approach with difference of convex (DC) programming and DC algorithm is proposed to indicate target location based on learning data. By measuring the variation in the received signal strength of the wireless links indicating the locations of the obstructions, the physical target in the monitoring area can be estimated through a learned dictionary. We show that the DFL problem can be efficiently formulated as a non-convex optimization problem. We adopt a penalty function called the minimax concave penalty, which possesses good properties in terms of seeking sparsity, and solve the non-convex optimization problem using DC programming. Furthermore, the localization accuracy achieved during the path-tracking task is further improved by the proposed tracking neighborhood rule. The rule provides a solution for increasing the localization accuracy under time-varying conditions generated by sampling channels of sensor networks under noisy conditions. The proposed approach is validated on a real-world dataset and has the potential to be adopted flexibly in DFL applications.