Novel weighted ensemble classifier for smartphone based indoor localization

作者：

Highlights：

• A novel weighted ensemble algorithm is proposed for indoor localization.

• Obtained around 95% accuracy even when train and test conditions are different.

• Incorporating mean and variance of RSSIs improve accuracy of the ensemble to 98%.

• The algorithm works for varying granularity: room level grid and 1m × 1m grid.

摘要

•A novel weighted ensemble algorithm is proposed for indoor localization.•Obtained around 95% accuracy even when train and test conditions are different.•Incorporating mean and variance of RSSIs improve accuracy of the ensemble to 98%.•The algorithm works for varying granularity: room level grid and 1m × 1m grid.

论文关键词：Indoor localization,Machine learning,Ensemble,Dempster–Shafer belief theory,WiFi,RSSI

论文评审过程：Received 6 May 2019, Revised 15 April 2020, Accepted 12 July 2020, Available online 3 August 2020, Version of Record 11 August 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113758