Removing useless APs and fingerprints from WiFi indoor positioning radio maps

• Main Authors: Eisa, Samih, Peixoto, Joao, Meneses, Filipe, Moreira, Adriano
• Format: Conference Proceeding
• Language: English
• Subjects: Area measurement; Filtering; Filtering positioning radio maps; Fingerprint recognition; IEEE 802.11 Standards; Phase measurement; Position measurement; Positioning engine performance; Science & Technology; Size measurement; WiFi indoor location
• DOI: 10.1109/IPIN.2013.6817919

Maintaining consistent radio maps for WiFi fingerprinting-based indoor positioning systems is an essential step to improve the performance of the positioning engines. The radio maps consist of WiFi fingerprints collected at a predefined set of positions/places within a positioning area. Each fingerprint consists of the identification and radio signal level of the surrounding Access Points (APs). Due to the wide proliferation of WiFi networks, it is very common to observe 10 to 20 APs at a single position and more than 50 APs across a single building. However, in practical, not all of the detected APs are useful for the position estimation process. Some of them might have weak signals at certain positions or might have less significance for a position's fingerprint. Thus, those useless APs will add additional computational overheads during the position estimation, and consequently they will reduce the overall performance of the positioning engines. A similar phenomenon also occurs with some of the collected fingerprints. While it is widely accepted that the larger and more detailed the radio map is, the better is the accuracy of the positioning system, we found that some of the fingerprint samples on the radio maps do not contribute significantly to the estimation process. In this paper, we propose two methods for filtering the positioning radio maps: APs filtering and Fingerprints filtering. Then we report on the results of a set of experiments that have been done to evaluate the performance of a WiFipositioning radio map before and after applying the filtering approaches. The results show that there is possibility to simplify the radio maps of the positioning engines without significant degradation on the positioning precision and accuracy, and therefore to reduce the processing time for estimating the position of a tracked WiFi tag. This result has an important impact on increasing the number of tags a single instance of a WiFi positioning engine can handle at a time.