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Research on Adaptive SVR Indoor Location Based on GA Optimization

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Abstract

Indoor positioning based on the received signal strength index of ZigBee has received more and more attention due to its low cost, low hardware power consumption and easy implementation. However, due to the existence of multi-path and shadow effects, traditional positioning algorithms often cannot achieve better positioning results. Support vector regression can use these relative errors as the characteristics of the fingerprint database to establish a regression map between the RSSI values and the positioning coordinates. Based on this, this paper proposes a genetic algorithm optimization support vector regression (GA-SVR) method to solve the problem of low ZigBee positioning accuracy. The penalty vector \(C\), RBF kernel width \(\sigma\), and loss function variable \(\in\) of support vector regression are optimized by genetic algorithm in the proposed method, so that support vector regression achieves the best position prediction performance. Firstly, the training data is clustered by Gaussian hybrid clustering algorithm to establish a fingerprint database. Then the GA-SVM classifier is used to classify the test points. Finally, the coordinates of the test points are regressed and predicted by the GA-SVR model. The simulation and experiment in the actual scene prove the effectiveness of the proposed method. The experimental results show that compared with PSO-SVR, GS-SVR, PLS-SVR, SVR and WKNN algorithms, the GA-SVR algorithm has higher positioning accuracy.

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References

  1. Kang, W., & Han, Y. (2015). SmartPDR: Smartphone-based pedestrian dead reckoning for indoor localization. IEEE Sensors Journal, 15(5), 2906–2916. https://doi.org/10.1109/JSEN.2014.2382568.

    Article  Google Scholar 

  2. Yang, Z., Wu, C. S., Zhou, Z. M., et al. (2015). Mobility increases localizability: A survey on wireless indoor localization using inertial sensors. ACM Computing Surveys, 47(3), 1–34. https://doi.org/10.1145/2676430.

    Article  Google Scholar 

  3. Kjærgaard, M. B., Blunck, H., & Godsk, T. (2010). Indoor positioning using GPS revisited. In Proceedings international conference on pervasive computing (pp. 38–56).

  4. Basiri, A., Lohan, E. S., Moore, T., & Winstanley, A. (2017). Indoor location based services challenges, requirements and usability of current solutions. Computer Science Review, 24, 1–12. https://doi.org/10.1016/j.cosrev.2017.03.002.

    Article  Google Scholar 

  5. Gikas, V., Dimitratos, A., & Perakis, H. (2016). Full-scale testing and performance evaluation of an active RFID system for position and personal mobility. In Proceedings international conference on IPIN (pp. 1–8), Alcalá de Henares, Spain, October 2016.

  6. Seco, F., Plagemann, C., & Jiménez, A. (2010). Improving RFID-based indoor positioning accuracy using gaussian processes. In Proceedings international conference on IPIN (pp. 1–8), Zurich, Switzerland, September 2010.

  7. Deng, Z., Fu, X., & Wang, H. (2018). An IMU-aided body-shadowing error compensation method for indoor bluetooth positioning. Sensors, 18(1), 1–21. https://doi.org/10.3390/s18010304.

    Article  Google Scholar 

  8. Huh, J., & Seo, K. (2017). An indoor location-based control system using bluetooth beacons for IoT systems. Sensors, 17(12), 1–22. https://doi.org/10.3390/s17122917.

    Article  Google Scholar 

  9. Yu, Z. Z., & Guo, G. Z. (2017). Improvement of positioning technology based on RSSI in ZigBee networks. Wireless Personal Communications, 95(3), 1943–1962. https://doi.org/10.1007/s11277-016-3860-1.

    Article  Google Scholar 

  10. Yuan, Y., Pei, L., & Xu, C. (2014). Efficient WiFi fingerprint training using semi-supervised learning. In Proceedings UPINLBS (pp. 148–155), Corpus Christ, USA, November 2014.

  11. Reddy, H., Chandra, M. G., & Balamuralidhar, P. (2007). An improved time-of-arrival estimation for WLAN-based local positioning. In Proceedings of 2nd international conference COMSWARE, Bangalore, India, January 2007.

  12. Cazzorla, A., De Angelis, G., & Moschitta, A. (2013). A 5.6-GHz UWB position measurement system. IEEE Transactions on Instrumentation and Measurement, 62(3), 675–683. https://doi.org/10.1109/tim.2012.2219139.

    Article  Google Scholar 

  13. Huang, Y., Zheng, J. Y., & Xiao, Y. (2015). Robust localization algorithm based on the RSSI ranging scope. International Journal of Distributed Sensor Networks. https://doi.org/10.1155/2015/587318.

    Article  Google Scholar 

  14. Kæmarungsi, K., & Krishnamurthy, P. (2012). Analysis of WLAN’s received signal strength indication for indoor location fingerprinting. Lecture Notes in Computer Science, 8(2), 292–316. https://doi.org/10.1016/j.pmcj.2011.09.003.

    Article  Google Scholar 

  15. Yang, L., Chen, H., & Cui, Q. (2015). Probabilistic-KNN: A novel algorithm for passive indoor-localization scenario. In Proceedings 81st IEEE VTC, Glasgow, UK, May 2015.

  16. Youssef, M. A., Agrawala, A., & Shankar, A. U. (2003). WLAN location determination via clustering and probability distributions. In Proceedings 1st IEEE international conference on pervasive computing and communications (pp. 143–150), Dallas-Fort Worth, USA, March 2003.

  17. Brunato, M., & Battiti, R. (2005). Statistical learning theory for location fingerprinting in wireless LANs. Computer Networks, 47(6), 825–845. https://doi.org/10.1016/j.comnet.2004.09.004.

    Article  MATH  Google Scholar 

  18. Dai, H., Ying, W. H., & Xu, J. (2016). Multi-layer neural network for received signal strength-based indoor localisation. IET Communications, 10, 717–723. https://doi.org/10.1049/iet-com.2015.0469.

    Article  Google Scholar 

  19. Laoudias, C., Panayiotou, C. G., & Kemppi, P. (2010). On the RBF-based positioning using WLAN signal strength fingerprints. In Proceedings 7th WPNC (pp. 93–98), Dresden, Germany, March 2010.

  20. Shu, Y., Huang, Y., & Zhang, J. (2016). Shin, gradient-based fingerprinting for indoor localization and tracking. IEEE Transactions on Industrial Electronics, 63(4), 2424–2433. https://doi.org/10.1109/tie.2015.2509917.

    Article  Google Scholar 

  21. Luo, J., & Zhan, X. (2014). Characterization of smart phone received signal strength indication for WLAN indoor positioning accuracy improvement. Journal of Networks, 9(3), 739–746. https://doi.org/10.4304/jnw.9.3.739-746.

    Article  Google Scholar 

  22. Haeberlen, A., Flannery, E., & Ladd, A. M. (2004). Practical robust localization overlarge-scale 802.11 wireless networks. In Proceedings 10th annual international conference on mobile computing and networking (pp. 70–84), Philadelphia, USA, 26 September through 1 October 2004.

  23. Cortes, C., & Vapnik, V. (1995). Support vector networks. Machine Learning, 20, 273–297. https://doi.org/10.1007/BF00994018.

    Article  MATH  Google Scholar 

  24. Figuera, C., Rojo-Alvarez, J. L., & Wilby, M. (2012). Advanced support vector machines for 802.11 indoor location. Signal Processing, 92(9), 2126–2136. https://doi.org/10.1016/j.sigpro.2012.01.026.

    Article  Google Scholar 

  25. Lin, S. W., Ying, K. C., Chen, S. C., et al. (2008). Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert Systems with Applications, 35(4), 1817–1824. https://doi.org/10.1016/j.eswa.2007.08.088.

    Article  Google Scholar 

  26. Kuo, R. J., Chen, C. M., Liao, T. W., et al. (2013). Hybrid of artificial immune system and particle swarm optimization-based support vector machine for radio frequency identification-based positioning system. Computers and Industrial Engineering, 64, 333–341. https://doi.org/10.1016/j.cie.2012.10.007.

    Article  Google Scholar 

  27. Yin, X. Y., Sun, Y. B., & Wang, C. H. (2013). Positioning errors predicting method of strapdown inertial navigation systems based on PSO-SVM. Abstract and Applied Analysis, 2013, 1–7. https://doi.org/10.1155/2013/737146.

    Article  MathSciNet  MATH  Google Scholar 

  28. Lv, Y., Liu, J. Z., & Yang, T. T. (2012). Nonlinear PLS integrated with error-based LSSVM and its application to NOx modeling. Industrial and Engineering Chemistry Research, 51(49), 16092–16100. https://doi.org/10.1021/ie3005379.

    Article  Google Scholar 

  29. Phan, A. V., Nguyen, M. L., & Bui, L. T. (2017). Feature weighting and SVM parameters optimization based on genetic algorithms for classification problems. Applied Intelligence, 46(2), 455–469. https://doi.org/10.1007/s10489-016-0843-6.

    Article  Google Scholar 

  30. Drucker, H., Burges, C. J. C., & Kaufman, L., et al. (1996). Support vector regression machines. [Online] http://papers.nips.cc/paper/1238-support-vector-regression-machines.pdf. Accessed 13 Feb 2019.

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Acknowledgements

This work was funded by North University of China of Key Laboratory Open Research Fund (No. DXMBJJ2018-08).

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Authors and Affiliations

  1. School of Information and Communication Engineering, North University of China, Taiyuan, China

    Xuming Liu, Wei Wang, Zhihui Guo, Cunhua Wang & Chen Tu

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  1. Xuming Liu
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  2. Wei Wang
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  3. Zhihui Guo
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Correspondence to Xuming Liu.

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Liu, X., Wang, W., Guo, Z. et al. Research on Adaptive SVR Indoor Location Based on GA Optimization. Wireless Pers Commun 109, 1095–1120 (2019). https://doi.org/10.1007/s11277-019-06605-6

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