基于貝葉斯網(wǎng)絡(luò)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(nèi)定位導(dǎo)航過程中單獨(dú)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(wǎng)絡(luò)的樓層定位算法。該算法先是利用擴(kuò)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(jìn)行融合，計算出行人垂直位移;然后利用誤差補(bǔ)償后的加速度積分特征對行人在樓梯中的轉(zhuǎn)角進(jìn)行檢測;最后，利用貝葉斯網(wǎng)絡(luò)融合行人行走高度和轉(zhuǎn)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(jié)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(zhǔn)確率提升6.81%;與平臺檢測算法相比，該算法的樓層定位準(zhǔn)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(zhǔn)確率達(dá)到99.36%。

關(guān)鍵詞：室內(nèi)定位;樓層定位;貝葉斯網(wǎng)絡(luò);擴(kuò)展卡爾曼濾波;轉(zhuǎn)角檢測

中圖分類號：?TP212.9

文獻(xiàn)標(biāo)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(wù)（Location Based Service， LBS）已經(jīng)應(yīng)用到很多室內(nèi)場景。比如，生活中老人和小孩位置的實時監(jiān)控、應(yīng)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(dǎo)航等。這些服務(wù)和功能的實現(xiàn)依賴于精確的室內(nèi)定位技術(shù)，但現(xiàn)有的室內(nèi)定位技術(shù)還無法滿足人們對室內(nèi)定位的需求，為此，眾多科研人員投身于室內(nèi)定位技術(shù)的研究。

當(dāng)前主流的室內(nèi)定位技術(shù)主要有Wi-Fi技術(shù)[1]、Zigbee[2]、超寬帶技術(shù)[3]、射頻識別[4]等，這些技術(shù)雖然定位精度較高，但在使用前需要預(yù)先部署設(shè)施，耗費(fèi)大量的成本;而且在發(fā)生突發(fā)情況時，這些技術(shù)難以及時發(fā)揮作用。……