基于互补滤波融合WiFi和PDR的行人室内定位
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摘要
提出了一种基于互补滤波融合Wi Fi和PDR的行人室内定位方法。首先改善Wi Fi位置指纹定位的KNN算法,通过阈值的设定,排除相似度高但实际上不可能的点,获取动态K值;然后通过行人航位推算(PDR)初始化算法,动态轨迹概率计算,确定PDR初始位置;最后在改进的Wi Fi和PDR的定位基础上,基于互补滤波原理,根据Wi Fi和PDR定位的不同特性,利用各自的定位优点,使用Wi Fi定位修正PDR的定位结果,通过相应权重参数的调整,输出最终融合定位结果。试验过程中,选取3种不同的室内环境区域,试验结果证明了该算法可大大提高室内定位的精度和稳定性。
This paper presents a pedestrian indoor location method based on complementary filtering fusion Wi Fi and PDR. Firstly,we improve the KNN algorithm of Wi Fi 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 Wi Fi and PDR,based on the principle of complementary filtering,according to the different characteristics of Wi Fi and PDR positioning,using their respective positioning advantages,using Wi Fi 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.
This paper presents a pedestrian indoor location method based on complementary filtering fusion Wi Fi and PDR. Firstly,we improve the KNN algorithm of Wi Fi 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 Wi Fi and PDR,based on the principle of complementary filtering,according to the different characteristics of Wi Fi and PDR positioning,using their respective positioning advantages,using Wi Fi 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.
引文
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[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.
[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 Wi Fi 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Wi Fi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.