基于貝葉斯網絡的樓層定位算法

張榜 朱金鑫 徐正蓺 劉盼 魏建明

摘 要：針對在室內定位導航過程中單獨依賴行人高度位移推測樓層位置誤差較大的問題，提出一種基于貝葉斯網絡的樓層定位算法。該算法先是利用擴展卡爾曼濾波（EKF）對慣性傳感器數(shù)據(jù)和氣壓計數(shù)據(jù)進行融合，計算出行人垂直位移;然后利用誤差補償后的加速度積分特征對行人在樓梯中的轉角進行檢測;最后，利用貝葉斯網絡融合行人行走高度和轉角信息推測行人在某一層的概率，從而將行人定位在建筑物中最可能出現(xiàn)的樓層上。實驗結果表明，與基于高度的樓層定位算法相比，所提算法的樓層定位準確率提升6.81%;與平臺檢測算法相比，該算法的樓層定位準確率提升14.51%;所提算法在總共1247次樓層變換實驗中，樓層定位準確率達到99.36%。

關鍵詞：室內定位;樓層定位;貝葉斯網絡;擴展卡爾曼濾波;轉角檢測

中圖分類號：?TP212.9

文獻標志碼：A

Bayesian network-based floor localization algorithm

ZHANG Bang1，2， ZHU Jinxin1，3， XU Zhengyi1，2*， LIU Pan1，2， WEI Jianming1

1.Shanghai Advanced Research Institute， Chinese Academy of Sciences，Shanghai 201210，China ;

2.University of Chinese Academy of Sciences，Beijing 100049，China ;

3.School of Communication and Information Engineering， Shanghai University， Shanghai 200444， China

Abstract：?In the process of indoor positioning and navigation， a Bayesian network-based floor localization algorithm was proposed for the problem of large error of floor localization when only the pedestrian height displacement considered. Firstly， Extended Kalman Filter （EKF） was adopted to calculate the vertical displacement of the pedestrian by fusing inertial sensor data and barometer data. Then， the acceleration integral features after error compensation was used to detect the corner when the pedestrian went upstairs or downstairs. Finally， Bayesian network was introduced to locate the pedestrian on the most likely floor based on the fusion of walking height and corner information. Experimental results show that， compared with the floor localization algorithm based on height displacement， the proposed algorithm has improved the accuracy of floor localization by 6.81%; and compared with the detection algorithm based on platform， the proposed algorithm has improved the accuracy of floor localization by 14.51%. In addition， the proposed algorithm achieves the accuracy of floor localization by 99.36% in the total 1247 times floor changing experiments.

Key words：?indoor positioning; floor localization; Bayesian network; Extended Kalman Filter （EKF）; corner detection

0 引言

近年來，基于位置的服務（Location Based Service， LBS）已經應用到很多室內場景。比如，生活中老人和小孩位置的實時監(jiān)控、應急情況下救援人員的快速救援、大型商場中的實時定位與導航等。這些服務和功能的實現(xiàn)依賴于精確的室內定位技術，但現(xiàn)有的室內定位技術還無法滿足人們對室內定位的需求，為此，眾多科研人員投身于室內定位技術的研究。

當前主流的室內定位技術主要有Wi-Fi技術[1]、Zigbee[2]、超寬帶技術[3]、射頻識別[4]等，這些技術雖然定位精度較高，但在使用前需要預先部署設施，耗費大量的成本;而且在發(fā)生突發(fā)情況時，這些技術難以及時發(fā)揮作用?！?br>