Abstract
With the addition of the Fine Timing Measurement (FTM) protocol in IEEE 802.11-2016, a promising sensor for smartphone-based indoor positioning systems was introduced. FTM enables a Wi-Fi device to estimate the distance to a second device based on the propagation time of the signal. Recently, FTM has gotten more attention from the scientific community as more compatible devices become available. Due to the claimed robustness and accuracy, FTM is a promising addition to the often used Received Signal Strength Indication (RSSI). In this work, we evaluate FTM on the 2.4 GHz band with 20 MHz channel bandwidth in the context of realistic indoor positioning scenarios. For this purpose, we deploy a least-squares estimation method, a probabilistic positioning approach and a simplistic particle filter implementation. Each method is evaluated using FTM and RSSI separately to show the difference of the techniques. Our results show that, although FTM achieves smaller positioning errors compared to RSSI, its error behavior is similar to RSSI. Furthermore, we demonstrate that an empirically optimized correction value for FTM is required to account for the environment. This correction value can reduce the positioning error significantly.
Highlights
Many indoor positioning systems (IPS) rely on radio frequency (RF) technologies for estimating a pedestrian’s position inside a building [1]
The most widespread approaches are those based on Received Signal Strength Indication (RSSI) provided by Wi-Fi
By measuring the RSSI emitted from Wi-Fi access points (AP) at known locations, the receiver, and the pedestrian can be located using the principles of multilateration
Summary
Many indoor positioning systems (IPS) rely on radio frequency (RF) technologies for estimating a pedestrian’s position inside a building [1]. The most widespread approaches are those based on Received Signal Strength Indication (RSSI) provided by Wi-Fi. Many buildings nowadays offer a good Wi-Fi infrastructure and standard smartphones can be used as the to be located receiver. Many buildings nowadays offer a good Wi-Fi infrastructure and standard smartphones can be used as the to be located receiver These two features make Wi-Fi interesting for the application in the consumer market, e.g., navigating inside an airport or shopping mall. As a simple RSSI-based multilateration is prone to errors in real-world scenarios, causing unacceptable inaccuracies, it is necessary to add more advanced methods to approach the positioning problem in a more accurate and stable way. The two most popular methods are fingerprinting and signal strength prediction [2,3]
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