Passive Indoor Localization with WiFi Fingerprints

Abstract

This paper proposes passive WiFi indoor localization. Instead of using WiFi signals received by mobile devices as fingerprints, we use signals received by routers to locate the mobile carrier. Consequently, software installation on the mobile device is not required. To resolve the data insufficiency problem, flow control signals such as request to send (RTS) and clear to send (CTS) are utilized. In our model, received signal strength indicator (RSSI) and channel state information (CSI) are used as fingerprints for several algorithms, including deterministic, probabilistic and neural networks localization algorithms. We further investigated localization algorithms performance through extensive on-site experiments with various models of phones at hundreds of testing locations. We demonstrate that our passive scheme achieves an average localization error of ∼0.8 m when the phone is actively transmitting data frames and ∼1.5 m when it is not transmitting data frames.

A Wi-Fi-Based Passive Indoor Positioning System via Entropy-Enhanced Deployment of Wi-Fi Sniffers

“…Previous studies [ 23 , 36 ] show that different models of DUTs perform differently in terms of positioning because each DUT has a different RSSI distribution. This occurs because different DUTs have different characteristics, such as the performance of the Wi-Fi antenna.…”

An Adaptive User Tracking Algorithm Using Irregular Data Frames for Passive Fingerprint Positioning

“…Reference [ 15 ] focused on a passive fingerprint positioning method with flow control signals, such as RTS/CTS and data frames accessing infra AP kernels. The research achieved 0.8 m/1.5 m of positioning errors by data frames in used/unused scenarios utilizing RSSI and CSI in an office environment.…”