Abstract
In traditional indoor localization, fingerprint localization algorithm fully considers the influences of multipath signals and static obstacles but degrades in case of the changes of observation environment. Trilateration localization has the better robustness to the signal variations but its performance degrades for achieving a certain level of accuracy. In this paper, for the problems above, firstly, a weighted fusion algorithm based on fingerprint recognition algorithm and trilateration algorithm was proposed. Then, adaptive fusion is added to filter the error localization point. Finally, linear Kalman filtering which based on the constant velocity state model assumption is introduced to smooth Wi-Fi localization error. By using the algorithms above, the experimental platform is set up to carry out the localization test. The test result justify that our proposed algorithm has performed better and achieved a better level of accuracy.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Farivar R, Wiczer D, Gutierrez A, Roy H (2009) A statistical study on the impact of wireless signals’ behavior on location estimation accuracy in 802.11 fingerprinting systems. In: IEEE international symposium on parallel & distributed processing, Rome, Italy, pp 1–8, 23–29 May
Zhou Z, Shen Y, Li B (2011) Moving time-window based real-time estimation algorithm for the stochastic model of GPS/Doppler navigation. Acta Geodaetica Cartogr Sin 40(2):220–225
Roshanaei M, Maleki M (2009) Dynamic-KNN: a novel locating method in WLAN based on angle of arrival. In: IEEE symposium on industrial electronics and applications, Kuala Lumpur, Malaysia, pp 722–726, 4–6 Oct
Liang P, Krause P (2016) Smartphone-based real-time indoor location tracking with 1-m Precision. IEEE J Biomed Health Inf 20(3):756–762
Zhang M, Shen W, Yao Z, Zhu J (2016) Multiple information fusion indoor location algorithm based on WIFI and improved PDR. In: Chinese control conference, Chengdu, China, pp 5086–5092, 27–29 July
Biswa J, Veloso M (2010) WiFi localization and navigation for autonomous indoor mobile robots. In: IEEE international conference on robotics and automation anchorage convention district, Anchorage, Alaska, USA, pp 4379–4384, 3–7 May
Yang Y, Gao W (2006) An optimal adaptive Kalman filter. J Geodesy 80(4):177–183
Chen B, Liu R, Chen Y, Liu J, Jiang X, Liu D (2015) WIFI fingerprint based self-adaptive indoor location in the dynamic environment. Chin J Sensors Actuators 28(5):729–738
Joydeep B, Manuela V (2010) WiFi localization and navigation for autonomous indoor mobile robots. IEEE international conference on robotics and automation anchorage convention district, vol 58(8), pp 4379–4384
Koch KR, Yang Y (1998) Robust Kalman filter for rank deficient observation models. J Geodesy 72(7):436–444
Zhou Z, Li Y, Rizos C, Shen Y (2009) A robust integration of GPS and MEMS-INS through trajectory-constrained adaptive Kalman filtering. In: Proceedings of international technical meeting of the satellite division of the Institute of Navigation, vol 5538(1), pp 995–1003
Xu J, Song E, Luo Y, Zhu Y (2012) Optimal distributed Kalman filtering fusion algorithm without invertibility of estimation error and sensor noise covariances. IEEE Signal Process Lett 19(1):55–58
Zhou Z, Shen Y, Li B (2010) A windowing-recursive approach for GPS real-time kinematic positioning. GPS Solutions 14(4):365–373
Wu J, Zhou Z, Chen J, Fourati H, Li R (2016) Fast complementary filter for attitude estimation using low-cost MARG sensors. IEEE Sens J 16(18):6997–7007
Acknowledgements
This work is supported by the National Natural Science Funds of China (No. 41604025 and 41704029), Sichuan Province Science and Technology Project (No. 2016GZ0062), the State Key Laboratory of Geodesy and Earth’s Dynamics (Institute of Geodesy and Geophysics, CAS) Grant No. SKLGED2018-3-2-E and the Key Laboratory of Precise Engineering and Industry Surveying of National Administration of Surveying, Mapping and Geoinformation (PF2015-11).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Tian, J., Zhou, Z., Wu, J., Du, S., Xiang, C., Kuang, C. (2018). Synthetization of Fingerprint Recognition and Trilateration for Wi-Fi Indoor Localization Through Linear Kalman Filtering. In: Sun, J., Yang, C., Guo, S. (eds) China Satellite Navigation Conference (CSNC) 2018 Proceedings. CSNC 2018. Lecture Notes in Electrical Engineering, vol 497. Springer, Singapore. https://doi.org/10.1007/978-981-13-0005-9_30
Download citation
DOI: https://doi.org/10.1007/978-981-13-0005-9_30
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-0004-2
Online ISBN: 978-981-13-0005-9
eBook Packages: EngineeringEngineering (R0)