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

作者：Huang, Huakun; Zhao, Haoli; Li, Xiang; Ding, Shuxue^*; Zhao, Lingjun; Li, Zhenni

来源：IEEE Access, 2018, 6: 61782-61799.

DOI：10.1109/ACCESS.2018.2876034

• Device-free localization
• wireless sensor networks
• multi-targets
• sparse coding
• subspace
• iterative shrinkage-thresholding algorithm

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 autoencoder, 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 x 10(-3) s and 2.1 x 10(-4) s respectively, which indicates that the proposed algorithms outperform the other three ones.

• 出版日期2018
• 单位广东工业大学