RF-KELM indoor positioning algorithm based on WiFi RSS fingerprint

WiFi-based fingerprint indoor positioning technology has been widely concerned, but it has been facing the challenge of lack of robustness to signal changes, and the positioning service requires fast and accurate positioning estimation. Therefore, an random forest-kernel extreme learning machine (RF-KELM) positioning algorithm with good comprehensive performance is proposed in this paper. Both offline and online phases are included by this algorithm. In the offline phase, the original data of WiFi fingerprint is first transformed into a form more suitable for positioning. Then, access point (AP) selection is performed on the fingerprint database containing many useless APs, in which an RF which can evaluate the importance of features is used. Finally, the KELM is trained with the sub-database that have undergone data transformation and AP selection. In the online phase, firstly, the obtained signal is processed, and then the trained KELM is used to predict the position of the data processed signal. In this paper, the performance of the proposed RF-KELM positioning algorithm is thoroughly tested on a publicly available dataset, and the experimental results demonstrate that the proposed algorithm not only has high positioning accuracy and robustness, but also takes only 0.08 s to position online.