Fingerprint Database Optimization Method for Indoor Localization Based on Neighbor Mean Filter

Wi-Fi is widely used for network communication in our daily life here and there. In addition to communication, it is also the reference information that is widely used in indoor positioning, such as localization based on RSSI (Radio Signal Strength Indicator) fingerprint database and range based localization. However, the broadcasting signals from wireless access point suffers from the complex indoor environment, such as multi-path fading and non-homogeneous environment. And the received signals' strength at the mobile terminal is instable, which must influence the positioning precision in indoor localization. Although these factors are existed, localization based on RSSI fingerprint database are still used widely indoors. There are 2 stages in the RSSI fingerprint database localization, offline fingerprint database building and online positioning. And the quality of both fingerprint database and real time RSSI values influence the precision of localization. However, the RSSI based indoor localization algorithms existed are almost conducted on the original fingerprint database which is not optimized. Some abnormal RSSI values in fingerprint database must destroy the distribution of the RSSI road map and result in low precision. In order to decrease the influence of original data containing bizarre RSSI possibly, filtering technology should be applied for the database. Considering the geographic correlation among RSSI values in road map, the neighbor mean filter (NMF) is introduced to optimize the fingerprint database in this paper. The idea of NMF is simply to replace each pixel value in an image with the mean value of its neighbors, excluding itself. Thus, it has the advantage for filtering the bizarre RSSI values in fingerprint database. In the paper, the introduction of NMF algorithm is described firstly, then, the bizarre value of RSSI fingerprint database is analyzed and the optimization on fingerprint database with NMF is narrated, especially, the method of determining threshold for optimization. Second, the indoor localization with optimized fingerprint database is put forward, which includes the architecture for RSSI fingerprint database localization, cosine calculation of RSSI vectors and their similarity judgement, and the localization based on optimized fingerprint database. Through these measures, the abnormal RSSI values found by NMF method and replaced with mean RSSI values could make the fingerprint database more accurate for the indoor positioning. In the end, with Java programming language, the positioning system based on optimized fingerprint database is developed in Eclipse environment. Then both original RSSI fingerprint database and the database filtered are tested and experimented in the positioning system, and the result shows that the optimized RSSI fingerprint database can increase the accurate of indoor localization.