基于GF-KF修正<em>RSSI</em>的室内指纹定位方法

韩学法; 吴飞; 朱海; 鄢松; 胡锐

doi:DOI:10.13442/j.gnss.1008-9268.2020.03.011

基于GF-KF修正RSSI的室内指纹定位方法

上海工程技术大学电子电气工程学院,上海 201620

详细信息

作者简介:
韩学法 (1994—),男,硕士研究生,研究方向为室内定位.

通信作者:
吴飞 E-mail:fei_wu1@163.com

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出版历程
- 刊出日期: 2020-06-14

Indoor fingerprint positioning method based on RSSI modified by GF-KF

School of Electronic and Electrical Engineering, Shanghai University of Engineering Science,Shanghai 201620, China

摘要

摘要: 针对Wi-Fi信号易受噪声等外界不确定因素的影响以及移动终端接收信号强度指示（RSSI）与真实值存在偏差而导致定位精度不高的问题,本文提出了一种基于GF-KF修正RSSI的室内指纹定位方法．由于采集的RSSI不稳定,该方法利用RSSI类高斯分布的特性,对RSSI数据进行高斯拟合,以得到较为确定的RSSI值．在此基础上,引入卡尔曼滤波算法对拟合后的RSSI数据进行误差修正,结合加权K近邻（WKNN）匹配算法进行定位．实验结果表明:本文方法的平均定位误差为1.5 m,2.0 m以内的误差累积分布概率为90.06％,定位效果优于同类方法．
- 室内指纹定位 /
- RSSI /
- 高斯拟合 /
- 卡尔曼滤波算法 /
- WKNN
Abstract: Aiming at the problems that Wi-Fi signals are susceptible to external uncertainties such as noise, and the RSSI received by mobile terminals deviates from the true value, which results in low positioning accuracy, this paper proposes an indoor fingerprint positioning method based on RSSI modified by GF-KF. Because the collected RSSI is unstable, this method uses the characteristics of the RSSI like Gaussian distribution to perform a Gaussian fit on the RSSI data to obtain a relatively determined RSSI value. Based on this, a Kalman filter algorithm is introduced to correct the RSSI data after fitting, and the WKNN matching algorithm is used to locate. The experimental results show that the average positioning error of the method in this paper is 1.50 m, and the cumulative distribution probability of positioning errors within 2.0 m is 90.06％, and the positioning effect is better than similar methods.
- indoor fingerprint positioning /
- RSSI /
- Gaussian fitting /
- Kalman filtering algorithm /
- WKNN

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参考文献(22)

HUANG J Y, TSAI C H, HUANG S T. The next generation of GPS navigation systems［J］. Communications of the acm, 2012, 55(3): 84-93.DOI: 10.1145/2093548.2093570.

李霖,王伟,谭永滨,等. 导航与LBS关键技术标准化研究进展［J］. 测绘通报， 2014(5): 95-98,126.

FERREIRA A F G, FERNANDES D M A, CATARINO A P, et al. Localization and positioning systems for emergency responders: a survey［J］. IEEE communications surveys ＆ tutorials, 2017, 19(4): 2836-2870.DOI: 10.1109/COMST.2017.2703620.

XIA H, ZUO J, LIU S, et al. Indoor localization on smartphones using built-in sensors and map constraints［J］. IEEE transactions on instrumentation and measurement, 2019, 68(4): 1189-1198.DOI: 10.1109/TIU.2018.2863478.

吴雨,杨力,王梦茹,等. 基于Android平台的WiFi定位系统研究与实现［J］. 全球定位系统, 2016, 41(4): 90-93.

JIA B, HUANG B, GAO H, et al. Selecting critical WiFi APs for indoor localization based on a theoretical error analysis［J］. IEEE access, 2019(7): 36312-36321.DOI: 10.1109/ACCESS.2019.2905372.

YIN Z, JIANG X, YANG Z, et al. WUB-IP: a high-precision UWB positioning scheme for indoor multiuser applications［J］. IEEE systems journal, 2019, 13(1): 279-288.DOI: 10.1109/JSYST.2017.2766690.

SHI W G, DU J X, CAO X W, et al. IKULDAS: an improved kNNbased UHF RFID indoor localization algorithm for directional radiation scenario［J］. Sensors, 2019(19):968.DOI: 10.3309/S19040968.

BIANCHI V, CIAMPOLINI P, DE MUNARI I. RSSIbased indoor iocalization and identification for ZigBee wireless sensor networks in smart homes［J］. IEEE transactions on instrumentation and measurement, 2019, 68(2): 566-575.DOI: 10.1109/TIM.2018.2851675.

ZUO Z, LIU L, ZHANG L, et al. Indoor positioning based on bluetooth lowenergy beacons adopting graph optimization［J］. Sensors, 2018, 18(11): 3736.DOI: 10.3309/s18113736.

SHAH S B, CHEN Z, YIN F, et al. 3D weighted centroid algorithm ＆ RSSI ranging model strategy for node localization in WSN based on smart devices［J］. Sustainable cities and society, 2018(39): 298-308.DOI: 10.1016/j.scs.2018.02.022.

BURGESS S, KUANG Y, ASTROM K, et al. TOA sensor network selfcalibration for receiver and transmitter spaces with difference in dimension［J］. Signal processing, 2015(107): 33-42.DOI: 10.1016/j.sigpro.2014.05.034.

WU S X, ZHANG S J, XU K, et al. Neural network localization with TOA measurements based on error learning and matching［J］. IEEE access, 2019(7): 19089-19099.DOI:10.1109/ACCESS.2019.2897 153.

WU C, HOU H, WANG W, et al. TDOA based indoor positioning with NLOS identification by machine learning［C］//2018 10th International Conference on Wireless Communications and Signal Processing(WCSP),2018.DOI: 10.1109/WCSP.2018.8555654.

[KG*2]HE S, CHAN S H G. WiFi fingerprintbased indoor positioning: recent advances and comparisons［J］. IEEE communications surveys and tutorials， 2016, 18(1): 466-490.DOI: 10.1109/COMST.2015.2464084.

王磊,周慧,蒋国平,等. 基于WiFi的自适应匹配预处理WKNN算法［J］. 信号处理, 2015, 31(9): 1067-1074.

左仲亮. 基于WIFI指纹的手机室内定位系统设计与实现［D］. 合肥：安徽大学, 2018.

冯涛,阮超,郭凯旋,等. 基于归一化RSS和约束WKNN的WiFi指纹定位算法［J］. 传感器与微系统， 2018, 37(10): 127-129.

HU J, LIU D, YAN Z, et al. Experimental analysis on weight Knearest neighbor indoor fingerprint positioning［J］. IEEE internet of things journal, 2019, 6(1): 891-897.DOI: 10.1109/J107.2018.2864607.

杨斌,李灯熬,赵菊敏. 基于区域划分的局部更新指纹定位算法［J］. 计算机工程与应用, 2018, 54(17): 56-61.

张颖. 基于RSSI的室内位置指纹定位算法研究［D］. 太原:太原理工大学, 2019.

FARIZ N, JAMIL N, DIN M M. An improved indoor location technique using kalman filtering on RSSI［J］. Jounal of computational and theoretical nanoscience,2018,24(3):1591-1598.DOI: 10.1166/asl.2018.11116.

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