4.7 Article

An Accurate WiFi Indoor Positioning Algorithm for Complex Pedestrian Environments

期刊

IEEE SENSORS JOURNAL
卷 21, 期 21, 页码 24440-24452

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2021.3113376

关键词

Indoor localization; received signal strength; probabilistic linear discriminant analysis; flow state; fingerprinting localization

向作者/读者索取更多资源

This paper presents a precise WiFi fingerprinting indoor positioning algorithm using improved PLDA, which achieves lower localization error and can simulate different fingerprint maps based on personnel movement states. After simulation, although there is a slight increase in localization error, the workload during the offline training phase is significantly reduced.
This paper proposes a precise WiFi fingerprinting indoor positioning algorithm for complex pedestrian environments. We transform the disturbed received signal strength (RSS) from the original space to latent space using the improved probabilistic linear discriminant analysis (PLDA). In the latent space, Bayes rule is used to calculate the posterior probability of the similarity between the test point and the reference points, and the K reference points with the highest posterior probability are weighted to estimate the position. Actual on-site experiments involving three floors demonstrate that the mean localization error of the proposed algorithm is 1.38 m, which outperforms the Horus algorithm by 29% under the same test conditions. In addition, by studying the variability of mean value of RSS in different pedestrian environments, the fingerprint maps in different states of personnel movement are simulated. By using which, the average localization error of the proposed algorithm increases slightly to 1.63m, while the workload required during the offline training phase is significantly reduced.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据