Indoor localization based on hybrid Wi-Fi hotspots

Xu, X; Yu, T; Li, S

Abstract

Most existing indoor localization algorithms based
on Wi-Fi signals mainly rely on wireless access points (APs), i.e. hotspots, with fixed deployment, which are easily affected by the non-line of sight (NLOS) factors and the multipath effect. There also exist many other problems, such as positioning stability and blind spots, which can cause decline in positioning accuracy at certain positions, or even failure of positioning. However, it will increase the hardware cost by adding more static APs; if the localization mechanism integrates different wireless signals is adopted, it tends to cause high cost of positioning and long complex positioning process, etc. In this paper, we proposed a novel hybrid Wi-Fi access point-based localization algorithm (HAPLA), which utilizes the received signal strength indications
(RSSI) from static APs and dynamic APs to determine location scenes. It flexibly selects available AP signals and dynamically switches the positioning methods, thus to achieve efficient positioning. HAPLA only relies on the Wi-Fi signal strength values, which can reduce the cost of hardware and the complexity of localization system. The proposed method can also be able to effectively prevent interference from different signal sources. In
our test scenario, we deployed typical indoor scenes with the NLOS factors and the multipath effect for experiments. The experiments demonstrate the effectiveness of proposed method and the results show that, compared with the classic K nearest neighbor-based location algorithm (KNN) and the variance-based fingerprint distance adjustment algorithm (VFDA), HAPLA has better adaptability and higher positioning accuracy, and can effectively solve the problem of positioning blind spots.

Citation

Xu, X., Yu, T., & Li, S. (2017). Indoor localization based on hybrid Wi-Fi hotspots

• Journal Article Type: Article
• Conference Name: International Conference on Indoor Positioning and Indoor Navigation (IPIN)
• Start Date: Sep 18, 2017
• End Date: Sep 21, 2017
• Acceptance Date: Aug 1, 2017
• Online Publication Date: Nov 23, 2017
• Publication Date: Jul 15, 2017
• Journal: 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN)
• Print ISSN: 2471-917X
• Peer Reviewed: Peer Reviewed
• Keywords: fingerprint, WiFi, indoor localization
• Publisher URL: http://dx.doi.org/10.1109/IPIN.2017.8115924
• Related Public URLs: http://www.ipin2017.org/
• Additional Information: Title of Conference or Conference Proceedings : 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN)