Compressive sensing based indoor localization fingerprint collection and construction
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems
Link
https://link.springer.com/content/pdf/10.1007/s11276-023-03406-5.pdf
Reference40 articles.
1. Xie, Y., Wang, Y., Nallanathan, A., & Wang, L. (2016). An improved k-nearest-neighbor indoor localization method based on spearman distance. IEEE Signal Processing Letters, 23(3), 351–355. https://doi.org/10.1109/LSP.2016.2519607
2. Khatab, Z. E., Hajihoseini, A., & Ghorashi, S. A. (2018). A fingerprint method for indoor localization using autoencoder based deep extreme learning machine. IEEE Sensors Letters, 2(1), 1–4. https://doi.org/10.1109/LSENS.2017.2787651
3. Zhang, Y., Zhu, Y., Lu, M., & Chen, A. (2013). Using compressive sensing to reduce fingerprint collection for indoor localization. In 2013 IEEE wireless communications and networking conference (WCNC) (pp. 4540–4545). https://doi.org/10.1109/WCNC.2013.6555310
4. He, S., Chan, S.-H.G., Yu, L., & Liu, N. (2019). Maxlifd: Joint maximum likelihood localization fusing fingerprints and mutual distances. IEEE Transactions on Mobile Computing, 18(3), 602–617. https://doi.org/10.1109/TMC.2018.2841842
5. Donoho, D. L. (2006). Compressed sensing. IEEE Transactions on Information Theory, 52(4), 1289–1306. https://doi.org/10.1109/TIT.2006.871582
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