The indoor positioning technology is based on the hotpots of location based services (LBS). However, most indoor positioning systems are two-dimensional and couldn’t meet the requirements of today’s LBS. The complex indoor structures and environment determine the floor positioning rather than the altitude positioning in the vertical direction, so the floor identification is the key to three-dimensional indoor positioning systems. There are many restrictions for the existing floor identification methods based on barometer or inertial sensor. They need to get the comparable data in advance, or detect the test data changes in a certain period of time for accurate identification. The current floor identification methods based on ordinary Wi-Fi fingerprints are less effective in the complex environment. Therefore, a new floor identification method based on confidence interval of Wi-Fi signals was developed in this paper, which was divided into the offline stage and the online stage. In the offline stage, the dynamic Wi-Fi signal sequences were collected fast. Then, the adaptive partitioning of Wi-Fi signal intervals was carried out according to RSSI distribution characteristics in the multi-floor environment. Finally, the confidence levels were calculated and the database of fingerprints was constructed. In the online stage, the matching between the test fingerprints and those in the database was applied to obtain the confidence of APs on each floor monitored by the test fingerprints. The sums of the confidence of APs on each floor were calculated, and the floor corresponding to the maximum value was judged as the target floor. To verify the performance of the proposed method, it was compared with the majority voting committees, K-means, Naive Bayes and KNN methods. The results indicate that it was better than other methods in large complex indoor scenes. Its identification accuracy rate was 92.2% and the error rate was 7.8% only one floor away. Moreover, it also could significantly reduce the size of the fingerprint database and further improve the efficiency of algorithm.

中文翻译：

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基于Wi-Fi信号置信区间的快速楼层识别方法

室内定位技术基于位置服务（LBS）的热点。但是，大多数室内定位系统都是二维的，无法满足当今LBS的要求。复杂的室内结构和环境决定了地板的位置，而不是垂直方向的高度，因此地板识别是三维室内定位系统的关键。现有的基于气压计或惯性传感器的地板识别方法有很多限制。他们需要提前获取可比较的数据，或者检测特定时间段内测试数据的变化以进行准确识别。当前基于普通Wi-Fi指纹的楼层识别方法在复杂的环境中效果较差。因此，提出了一种基于Wi-Fi信号置信区间的楼层识别新方法，该方法分为离线阶段和在线阶段。在离线阶段，快速收集动态Wi-Fi信号序列。然后，根据多层环境中的RSSI分布特性对Wi-Fi信号间隔进行自适应划分。最后，计算置信度并建立指纹数据库。在在线阶段，应用测试指纹与数据库中指纹之间的匹配，以获取受测试指纹监控的每个楼层上的AP的置信度。计算各楼层的AP的置信度总和，并将与最大值相对应的楼层判断为目标楼层。为了验证所提出方法的性能，将其与多数投票委员会，K-means，朴素贝叶斯和KNN方法进行了比较。结果表明，在大型复杂室内场景中，该方法优于其他方法。它的识别准确率是92.2％，错误率是7.8％（仅相隔一层）。此外，它还可以显着减小指纹数据库的大小，并进一步提高算法的效率。