Received signal strength based indoor positioning algorithm using advanced clustering and kernel ridge regression


We propose a novel indoor positioning algorithm based on the received signal strength (RSS) fingerprint. The proposed algorithm can be divided into three steps, an offline phase at which an advanced clustering (AC) strategy is used, an online phase of approximate localization at which cluster matching is used, and an online phase of precise localization with kernel ridge regression. Specifically, after offline fingerprint collection and similarity measurement, we employ an AC strategy based on the K-medoids clustering algorithm using additional reference points that are geographically located at the outer cluster boundary to enrich the data of each cluster. During the approximate localization, RSS measurements are compared with the cluster radio maps to determine to which cluster the target most likely belongs. Both the Euclidean distance of the RSSs and the Hamming distance of the coverage vectors between the observations and training records are explored for cluster matching. Then, a kernel-based ridge regression method is used to obtain the ultimate positioning of the target. The performance of the proposed algorithm is evaluated in two typical indoor environments, and compared with those of state-of-the-art algorithms. The experimental results demonstrate the effectiveness and advantages of the proposed algorithm in terms of positioning accuracy and complexity.

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  1. Al-Moukhles H, Jaber AK, Abdel-Qader I, 2016. Impact of APs selection scheme on compressive sensing-fingerprinting based IPS performance. Proc IEEE 7th Annual Ubiquitous Computing, Electronics & Mobile Communication Conf, p.1–7.

  2. Al Nuaimi K, Kamel H, 2011. A survey of indoor positioning systems and algorithms. Proc Int Conf on Innovations in Information Technology, p. 185–190.

  3. Bahl P, Padmanabhan VN, 2000. RADAR: an in-building RF-based user location and tracking system. Proc IEEE Conf on Computer Communications and 19th Annual Joint Conf of the IEEE Computer and Communications Societies, p.775–784.

  4. Chen C, Wang YJ, Zhang Y, et al., 2018. Indoor positioning algorithm based on nonlinear PLS integrated with RVM. IEEE Sens J, 18(2):660–668.

    Article  Google Scholar 

  5. Chen YQ, Yang Q, Yin J, et al., 2006. Power-efficient accesspoint selection for indoor location estimation. IEEE Trans Knowl Data Eng, 18(7):877–888.

    Article  Google Scholar 

  6. Dai H, Ying WH, Xu J, 2016. Multi-layer neural network for received signal strength-based indoor localisation. IET Commun, 10(6):717–723.

    Article  Google Scholar 

  7. Fang SH, Lin T, 2012. Principal component localization in indoor WLAN environments. IEEE Trans Mob Comput, 11(1):100–110.

    Article  Google Scholar 

  8. Fang XM, Jiang ZH, Nan L, et al., 2018. Optimal weighted K-nearest neighbour algorithm for wireless sensor network fingerprint localisation in noisy environment. IET Commun, 12(10):1171–1177.

    Article  Google Scholar 

  9. Feng C, Au WSA, Valaee S, et al., 2012. Received-signal-strength-based indoor positioning using compressive sensing. IEEE Trans Mob Comput, 11(12):1983–1993.

    Article  Google Scholar 

  10. Harroud H, Ahmed M, Karmouch A, 2003. Policy-driven personalized multimedia services for mobile users. IEEE Trans Mob Comput, 2(1):16–24.

    Article  Google Scholar 

  11. Honeine P, Mourad F, Kallas M, et al., 2011. Wireless sensor networks in biomedical: body area networks. Proc Int Workshop on Systems, Signal Processing and Their Applications, p.388–391.

  12. Hu JS, Liu HL, Liu DW, et al., 2018. Reducing Wi-Fi fingerprint collection based on affinity propagation clustering and WKNN interpolation algorithm. Proc 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conf, p.2463–2468.

  13. Huang CC, Manh HN, 2016. RSS-based indoor positioning based on multi-dimensional kernel modeling and weighted average tracking. IEEE Sens J, 16(9):3231–3245.

    Article  Google Scholar 

  14. Khalajmehrabadi A, Gatsis N, Pack DJ, et al., 2017a. A joint indoor WLAN localization and outlier detection scheme using LASSO and elastic-net optimization techniques. IEEE Trans Mob Comput, 16(8):2079–2092.

    Article  Google Scholar 

  15. Khalajmehrabadi A, Gatsis N, Akopian D, 2017b. Structured group sparsity: a novel indoor WLAN localization, outlier detection, and radio map interpolation scheme. IEEE Trans Veh Technol, 66(7):6498–6510.

    Article  Google Scholar 

  16. Kumar C, Rajawat K, 2019. Dictionary-based statistical fingerprinting for indoor localization. IEEE Trans Veh Technol, 68(9):8827–8841.

    Article  Google Scholar 

  17. Kushki A, Plataniotis KN, Venetsanopoulos AN, et al., 2007. Kernel-based positioning in wireless local area networks. IEEE Trans Mob Comput, 6(6):689–705.

    Article  Google Scholar 

  18. Li LQ, He Z, Nielsen J, et al., 2015. Using Wi-Fi/magnetometers for indoor location and personal navigation. Proc Int Conf on Indoor Positioning and Indoor Navigation, p.1–7.

  19. Lu XX, Zou H, Zhou HM, et al., 2016. Robust extreme learning machine with its application to indoor positioning. IEEE Trans Cybern, 46(1):194–205.

    Article  Google Scholar 

  20. Maalouf M, Homouz D, 2014. Kernel ridge regression using truncated Newton method. Knowl-Based Syst, 71:339–344.

    Article  Google Scholar 

  21. Mahfouz S, Mourad-Chehade F, Honeine P, et al., 2013. Kernel-based localization using fingerprinting in wireless sensor networks. Proc IEEE 14th Workshop on Signal Processing Advances in Wireless Communications, p.744–748.

  22. Mahfouz S, Mourad-Chehade F, Honeine P, et al., 2016. Non-parametric and semi-parametric RSSI/distance modeling for target tracking in wireless sensor networks. IEEE Sens J, 16(7):2115–2126.

    Article  Google Scholar 

  23. Niu JW, Wang BW, Shu L, et al., 2015. ZIL: an energy-efficient indoor localization system using ZigBee radio to detect WiFi fingerprints. IEEE J Sel Areas Commun, 33(7):1431–1442.

    Article  Google Scholar 

  24. Rodriguez MD, Favela J, Martinez EA, et al., 2004. Location-aware access to hospital information and services. IEEE Trans Inform Technol Biomed, 8(4):448–455.

    Article  Google Scholar 

  25. Saunders C, Gammerman A, Vovk V, 1998. Ridge regression learning algorithm in dual variables. Proc 15th Int Conf on Machine Learning, p.515–521.

  26. Shi LF, Wang Y, Liu GX, et al., 2018. A fusion algorithm of indoor positioning based on PDR and RSS fingerprint. IEEE Sens J, 18(23):9691–9698.

    Article  Google Scholar 

  27. Wang XY, Gao LJ, Mao SW, et al., 2017. CSI-based finger-printing for indoor localization: a deep learning approach. IEEE Trans Veh Technol, 66(1):763–776.

    Google Scholar 

  28. Wu Z, Fu KC, Jedari E, et al., 2016. A fast and resource efficient method for indoor positioning using received signal strength. IEEE Trans Veh Technol, 65(12):9749–9758.

    Google Scholar 

  29. Xue WX, Yu KG, Hua XH, et al., 2018. APs’ virtual positions-based reference point clustering and physical distance-based weighting for indoor Wi-Fi positioning. IEEE Intern Things J, 5(4):3031–3042.

    Article  Google Scholar 

  30. Yan J, Zhao L, Tang J, et al., 2018. Hybrid kernel based machine learning using received signal strength measurements for indoor localization. IEEE Trans Veh Technol, 67(3):2824–2829.

    Article  Google Scholar 

  31. Youssef MA, Agrawala A, Shankar AU, 2003. WLAN location determination via clustering and probability distributions. Proc 1st IEEE Int Conf on Pervasive Computing and Communications, p.143–150.

  32. Zhang Y, Li DP, Wang YJ, 2019. An indoor passive positioning method using CSI fingerprint based on Adaboost. IEEE Sens J, 19(14):5792–5800.

    Article  Google Scholar 

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Corresponding author

Correspondence to Heng Yao.

Additional information

Project supported by the National Natural Science Foundation of China (Nos. 51705324 and 61702332)


Yanfen LE and Heng YAO designed the research. Yanfen LE and Hena ZHANG processed the data. Yanfen LE drafted the manuscript. Weibin SHI helped organize the manuscript. Yanfen LE and Heng YAO revised and finalized the paper.

Compliance with ethics guidelines

Yanfen LE, Hena ZHANG, Weibin SHI, and Heng YAO declare that they have no conflict of interest.

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Le, Y., Zhang, H., Shi, W. et al. Received signal strength based indoor positioning algorithm using advanced clustering and kernel ridge regression. Front Inform Technol Electron Eng (2021).

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Key words

  • Indoor positioning
  • Received signal strength (RSS) fingerprint
  • Kernel ridge regression
  • Cluster matching
  • Advanced clustering

CLC number

  • TN92