An Accurate and Efficient Device-Free Localization Approach Based on Sparse Coding in Subspace

In practical device-free localization (DFL) applications, for enlarging the monitoring area and improving localization accuracy, too many nodes need to be deployed, which results in a large volume of DFL data with high dimensions. This arises a key problem of seeking an accurate and efficient approa...

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Veröffentlicht in:IEEE access Jg. 6; S. 61782 - 61799
Hauptverfasser: Huang, Huakun, Zhao, Haoli, Li, Xiang, Ding, Shuxue, Zhao, Lingjun, Li, Zhenni
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Coders
Coding
Device-free localization
Encoding
Iterative algorithms
Iterative methods
iterative shrinkage-thresholding algorithm
Localization
Machine learning
Matched pursuit
Matching pursuit algorithms
Monitoring
multi-targets
Multiple target tracking
sparse coding
subspace
Subspaces
Target tracking
Wireless communication
Wireless sensor networks
ISSN:2169-3536, 2169-3536
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Zusammenfassung:In practical device-free localization (DFL) applications, for enlarging the monitoring area and improving localization accuracy, too many nodes need to be deployed, which results in a large volume of DFL data with high dimensions. This arises a key problem of seeking an accurate and efficient approach for DFL. In order to address this problem, this paper regards DFL as a problem of sparse-representation-based classification; builds a sparse model; and then proposes two sparse-coding-based algorithms. The first algorithm, sparse coding via the iterative shrinkage-thresholding algorithm (SC-ISTA), is efficient for handling high-dimensional data. And then, subspace techniques are further utilized, followed by performing sparse coding in the low-dimensional signal subspace, which leads to the second algorithm termed subspace-based SC-ISTA (SSC-ISTA). Experiments with the real-world data set are conducted for single-target and multi-target localization, and three typical machine learning algorithms, deep learning based on auto encoder, K-nearest neighbor, and orthogonal matching pursuit, are compared. Experimental results show that both SC-ISTA and SSC-ISTA can achieve high localization accuracies of 100% and are robust to noisy data when SNR is greater than 10 dB, and the time costs for sparse coding of SC-ISTA and SSC-ISTA are 2.1 × 10 -3 s and 2.1 × 10 -4 s respectively, which indicates that the proposed algorithms outperform the other three ones.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2018.2876034