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摘要: 作为室内位置服务的基础，室内定位技术近年来得到了广泛的关注。针对现有室内定位技术存在成本高、精度有限以及效率不足等问题，提出了一种融合人类活动识别、行人航迹推算(pedestrian dead recko-ning，PDR)以及地标匹配修正等技术的室内行人位置推算方法。该方法使用基于智能手机的PDR技术来估算用户的位置信息，而人类活动识别技术则用来感知用户室内移动行为中的特定地标，利用这些地标信息来辅助修正PDR轨迹中产生的累积误差。此外，为了解决用户初始位置未知的问题，引入隐式马尔科夫模型进行推断，并提出了一种顾及室内环境特征的维特比算法来确定用户轨迹。实验结果显示，所提方法在提高室内行人移动行为识别和定位精度的同时，有效实现了用户室内轨迹的追踪。Abstract: As the basis of indoor location services, indoor localization technology has received more and more attention in recent years. Aiming at the problems of high cost, limited precision and insufficient efficiency in existing indoor positioning technologies, pedestrian dead reckoning (PDR), human acti-vity recognition (HAR) and landmarks are combined to obtain more accurate pedestrian indoor localization. PDR is used to estimate the user's location, and the cumulative error of PDR is reduced by landmarks, which are sensed by HAR. In addition, to solve the initial position determination problem, a hidden Markov model that considers the characteristics of the indoor environment is applied to match the continuous trajectory. The experimental results show that the proposed method has a good performance in activity recognition and positioning accuracy, and can track the user's trajectory efficiently.
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图 5 真实轨迹108、PDR原始轨迹和修正轨迹
Figure 5. True Trajectory 108, PDR Original Trajectory and Corrected Trajectory
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图 6 轨迹201的PDR估计轨迹与修正轨迹
Figure 6. PDR Trajectory and Corrected Trajectory of Trajectory 201
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图 8 轨迹108的PDR位置误差以及通过地标修正后的位置误差
Figure 8. Errors from PDR Location and Location Errors Corrected by Landmarks of Trajectory 108
表 1 分类精度/%
Table 1 Classification Accuracy/%
分类方法 基于滑动窗口 基于单步事件窗口 DT 98.62 98.69 SVM 96.55 97.73 KNN 98.83 98.95 
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表 2 kNN分类方法的混淆矩阵
Table 2 Confusion Matrix of kNN
行为类别 预测类别 分类精度/% 站立 行走 上下楼 开门 站立 284 (294) 0 0 10 (0) 96.60 (100) 行走 0 1981 3 0 99.85 上下楼 0 4 787 0 99.49 开门 3 (0) 0 0 56 (59) 94.92 (100)
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