Integration of WiFi and PDR based complementary filtering indoor localization

doi:10.13474/j.cnki.11-2246.2019.0140

Abstract

Abstract: This paper presents a pedestrian indoor location method based on complementary filtering fusion WiFi and PDR. Firstly, we improve the KNN algorithm of WiFi position fingerprint location. By setting threshold, we can get the dynamic K value by eliminating the points with high similarity but practically impossible. Secondly, we determine the initial position of PDR by initializing the pedestrian dead reckoning (PDR) algorithm and calculating the dynamic trajectory probability. Finally, on the basis of the improved positioning of WiFi and PDR, based on the principle of complementary filtering, according to the different characteristics of WiFi and PDR positioning, using their respective positioning advantages, using WiFi positioning to modify the positioning results of PDR, through adjusting the corresponding weight parameters, the final fusion positioning results are output. During the experiment, we choose three different indoor environment areas. The experimental results show that the algorithm can greatly improve the accuracy and stability of indoor positioning.

Key words: indoor localization, location fingerprint, pedestrian dead reckoning, complementary filtering

CLC Number:

P228

ZHU Jiasong, CHENG Kai, ZHOU Baoding, LIN Weidong. Integration of WiFi and PDR based complementary filtering indoor localization[J]. 测绘通报, 2019, 0(5): 12-15,34.

References

[1] 吴楠,王旭东,胡晴晴,等.基于多LED的高精度室内可见光定位方法[J].电子与信息学报,2015,37(3):727-732.
[2] 周宝定,李清泉,毛庆洲,等.用户行为感知辅助的室内行人定位[J].武汉大学学报(信息科学版), 2014,39(6):719-723.
[3] ZHOU B, LI Q, MAO Q, et al.Activity sequence-based indoor pedestrian localization using smartphones[J]. IEEE Transactions on Human-Machine Systems, 2015,45(5):562-574.
[4] 徐玉滨,邓志安,马琳.基于核直接判别分析和支持向量回归的WLAN室内定位算法[J].电子与信息学报, 2011,33(4):896-901.
[5] 余文婕. 一种基于RSS的WIFI室内定位的研究方法[J]. 科技视界,2018(8):269-272.
[6] HUANG F S, XIAO S, CHENG F, et al. Comparison of common algorithm of WiFi indoor location based on RSSI[J]. Information Technology, 2017(12):73-75.
[7] LIU H, DARABI H, BANERJEE P, et al.Survey of wireless indoor positioning techniques and systems[J].IEEE Transactions on Systems, Man, and Cybernetics, 2007, 37(6):1067-1080.
[8] PEI L, CHEN R, LIU J, et al. Using inquiry-based bluetooth RSSI probability distributions for indoor positioning[J]. Journal of Global Positioning Systems, 2010, 9(2):122-130.
[9] HUANG P F. Implementation of high-precision clock synchronous system based on NTP[J]. Application of Electronic Technique, 2009, 35(7):122-123.
[10] BAHL P, PADMANABHAN V N. RADAR:an in-building RF-based user location and tracking system[C]//Proceedings of the 19th Annual Joint Conference of the IEEE Computer and Communications Societies. Tel Aviv:IEEE, 2000:775-784.
[11] KUSHKI A, PLATANIOTIS K N, VENETSANOPOULOS A N. Kernel-based positioning in wireless local area networks[J].IEEE Transactions on Mobile Computing, 2007, 6(6):689-705.
[12] YOUSSEF M, AGRAWALA A. The Horus WLAN location determination system[C]//Proceedings of the 3rd International Conference On Mobile Systems, Applications, and Services. Seattle, WA:ACM, 2005:205-218.
[13] BRUNATO M, BATTITI R. Statistical learning theory for location fingerprinting in wireless LANs[J]. Computer Networks, 2005, 47(6):825-845.
[14] 张世哲. 基于惯性传感器和WiFi的室内定位系统的设计与实现[D].北京:北京邮电大学,2012.
[15] 宋欣.多传感融合的室内定位技术研究[D].上海:上海交通大学,2013.
[16] LEE S, CHON Y, CHA H. Smartphone-based indoor pedestrian tracking using geo-magnetic observations[J]. Mobile Information Systems, 2013, 9(2):123-137.