| 359 | 8 | 50 |
| 下载次数 | 被引频次 | 阅读次数 |
行人航位推算(Pedestrian Dead Reckoning, PDR)具有实现简单、成本低、短时间内定位精度较高等优点。针对PDR定位中存在的误差累积问题,从步频和航向估计两方面进行改进。提出了相邻波峰(波谷)二次判断及多重阈值联合的步频推算方法,步数估计的准确率可以达到98%;针对航向漂移问题,利用卡尔曼滤波对加速度传感器、磁力计和陀螺仪进行融合,航向平均误差为13.6°。实验表明,利用提出的多传感器融合的PDR算法,在多次转弯的情况下定位精度可以达到1.8 m。
Abstract：Pedestrian Dead Reckoning(PDR) has the advantages of simple implementation, low cost, and high positioning accuracy in a short time. Aiming at the problem of error accumulation in PDR positioning, improvement was made from two aspects: cadence and heading estimations. A step frequency estimation method combining the secondary judgment of adjacent peaks(wave troughs) and multiple thresholds is proposed. The accuracy of the step estimation can reach 98%. For the heading drift problem, the Kalman filter is used for the acceleration sensor, magnetometer and gyroscope. After fusion, the average heading error is 13.6°. Experiments show that by using the proposed multi-sensor fusion PDR algorithm, the positioning accuracy can reach 1.8 m in the case of multiple turns.
[1] 陈斌涛,刘任任,陈益强,等.动态环境中的 WiFi 指纹自适应室内定位方法[J].传感技术学报,2015,28(5):729-738.
[2] Feldmann S,Kyamakya K,Zapater A,et al.An indoor Bluetooth-based positioning system:Concept,implementation and experimental evaluation[J].Proceedings of the International Conference on Wireless Networks,2003,109-113.
[3] Sugano M,Kawazoe T,Ohta Y,et al.Indoor Localization System using RSSI Measurement of Wireless Sensor Network based on ZigBee Standard[J].Wireless and Optical Communications,2006(538):1-6.
[4] Wang H,Sen S,Mariakakis A,et al.Demo:unsupervised indoor localization[C]//Proceedings of the 10th international conference on Mobile systems,applications,and services-MobiSys’12.June 25-29,2012.Low Wood Bay,Lake District,UK.New York:ACM Press,2012:499-500.
[5] Li F,Zhao C S,Ding G Z,et al.A reliable and accurate indoor localization method using phone inertial sensors[C]//UbiComp’ 12:Proceedings of the 2012 ACM Conference on Ubiquitous Computing.2012:421-430.
[6] 田国会,张庆宾,丁娜娜.基于WT-UKF的PDR/GPS组合定位算法[J].控制与决策,2015,30(1):86-90.
[7] 徐龙阳.一种基于智能手机的行人航位推算室内定位方法[J].电脑知识与技术,2017,13(36):198-199.
[8] 李明峰,王珅,檀丁.基于WiFi/PDR的室内定位初始位置融合解算方法研究[J].大地测量与地球动力学,2019,39(6):602-606.
[9] 谷亚军.基于Wi-Fi与多传感器数据融合的室内定位优化方法研究[D].北京:北京交通大学,2018.
[10] 刘春燕.基于多源信息融合的行人航位推算室内定位方法[J].中国惯性技术学报,2016,24(2):208-214.
[11] 刘庆,关维国,李顺康,等.基于扩展Kalman滤波的室内WiFi-PDR融合定位算法[J].计算机工程,2019,45(4):66-71.
[12] 徐龙阳,郑增威,孙霖,等.基于神经网络的多传感器融合PDR定位方法[J].传感技术学报,2018,31(4):579-587.
[13] 陈国良,杨洲.基于加速度量测幅值零速检测的计步算法研究[J].武汉大学学报.信息科学版,2017,42(6):726-730.
[14] 朱庄生,张雨龙,李驰.基于MEMS惯性测量单元的多源信息自适应步数检测方法[J].中国惯性技术学报,2017,25(3):299-303.
[15] Link J á B,Smith P,Viol N,et al.FootPath:Accurate map-based indoor navigation using smartphones[C]//2011 International Conference on Indoor Positioning and Indoor Navigation.September 21-23,2011,Guimaraes,Portugal.IEEE,2011:1-8.
[16] Fan Q G,Zhang H,Pan P,et al.Improved pedestrian dead reckoning based on a robust adaptive Kalman filter for indoor inertial location system[J].Sensors,2019,19(2):294.
基本信息:
中图分类号:TP212.9;TN713
引用信息:
[1]曹娟,崔学荣,李娟,等.多传感器融合的行人航位推算方法研究[J].微型电脑应用,2021,37(03):1-3+9.
基金信息:
国家自然科学基金项目(61902431); 山东省重点研发项目(2019GGX101048); 中央高校基本科研业务费专项基金项目(17CX02042A,19CX05003A-9,18CX02136A); 潍坊市科技计划项目(2019ZJ1063)
2021-03-20
2021-03-20