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Compressive sensing based indoor localization fingerprint collection and construction

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Abstract

Localization based on fingerprint has been viewed as a popular indoor localization technique, which uses the signal strength of different positions as the location fingerprint. The localization model can thus be constructed by analyzing the relationship between the location fingerprint and the target location. However, this method requires the manual acquisition of fingerprint signal data in an offline phase, which has become a bottleneck for practical application, especially in large-scale fields. Therefore, how to reduce the workload in fingerprint collection has become a significant issue. This paper invokes a compressive sensing-based method to reduce fingerprint construction complexity. First, the k-singular value decomposition algorithm based on an overcomplete dictionary is employed to sparse the fingerprint signal. Then, considering the uncertainty of the signal sparsity in the indoor environment, an adaptive fingerprint signal reconstruction algorithm based on error weight is proposed to construct signals with variable sparsity. We test the proposed fingerprint reconstruction on both actual RSSI and geomagnetic fingerprints. Experiments show that the fingerprint database of 132 reference positions can be reconstructed with only 50 compressed samples, which reduces the workload of offline collection by 62\(\%\).

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants Nos. 61972079, 62172084, 62132004, in part by the Major Research Plan of National Natural Science Foundation of China under Grant No. 92167103, in part by the LiaoNing Revitalization Talents Program under Grant No. XLYC2007162, in part by the LiaoNing Key Research and Development Program under Grant No. 2023JH2/101300196, in part by the Central Government Guided Local Science and Technology Development Fund Project under Grant No. 2020ZY0003, in part by the Science and Technology Plan Project of Inner Mongolia Autonomous Region of China under Grant No. 2020GG0189, and in part by the Fundamental Research Funds for the Central Universities under Grants Nos. N2224001-7, N2216009, N2216006, and N2116004.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Northeastern University, Shenyang, 100190, Liaoning, China

    Jie Jia, Haowen Guan, Jian Chen, Leyou Yang, An Du & Xingwei Wang

  2. Engineering Research Center of Security Technology of Complex Network System, Ministry of Education, Shenyang, 110819, Liaoning, China

    Jie Jia

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  1. Jie Jia
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Jia, J., Guan, H., Chen, J. et al. Compressive sensing based indoor localization fingerprint collection and construction. Wireless Netw 30, 51–65 (2024). https://doi.org/10.1007/s11276-023-03406-5

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