Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
119 tokens/sec
GPT-4o
56 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Passive Indoor Localization with WiFi Fingerprints (2111.14281v1)

Published 29 Nov 2021 in eess.SP and cs.NI

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.

User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (8)
  1. Minh Tu Hoang (6 papers)
  2. Brosnan Yuen (11 papers)
  3. Kai Ren (18 papers)
  4. Ahmed Elmoogy (2 papers)
  5. Xiaodai Dong (39 papers)
  6. Tao Lu (72 papers)
  7. Robert Westendorp (7 papers)
  8. Kishore Reddy Tarimala (1 paper)
Citations (3)

Summary

We haven't generated a summary for this paper yet.