High-Dimensional Optimization and Probability - With a View Towards Data Science
High-Dimensional Optimization and Probability - With a View Towards Data Science
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高维优化和概率 - 着眼于数据科学
DOI：
10.1007/978-3-031-00832-0_12
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Aboushelbaya R
中科院分区：
文献类型：
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作者：
Aboushelbaya R
在本章中，首次提出了一种基于压缩感知(CS)的下采样位置数据稀疏重建方案。研究了位置数据的稀疏性，给出了两种基于套索回归和神经网络的路径重建算法，该算法仅需对∼接收机进行20%的采样即可有效地重建路径。讨论了iOS设备的实现，并将其结果作为CS在物联网(IoT)设备基于位置的跟踪中的适用性的概念证明。
In this chapter, a scheme based on compressive sensing (CS) for the sparse reconstruction of down-sampled location data is presented for the first time. The underlying sparsity properties of the location data are explored and two algorithms based on LASSO regression and neural networks are shown to be able to efficiently reconstruct paths with only ∼20% sampling of the GPS receiver. An implementation for iOS devices is discussed and results from it are shown as proof of concept of the applicability of CS in location-based tracking for Internet of Things (IoT) devices.