Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Fingerprint Data Collection <s> We previously developed an analytical model in [8] to predict the precision and accuracy performance of indoor positioning systems using location fingerprints. A by-product of the model is the ability to eliminate unnecessary fingerprints to reduce the number of fingerprints in a radio map for comparison without loss in performance. This model enables computation of an approximate probability distribution of location selection, by employing a proximity graph to extract neighbor and non-neighbor sets of a given fingerprint. However, employing the model 'as is' in a system with many location fingerprints requires determining a single large proximity graph derived from all location fingerprints which may involve significant computational effort. In this paper, we consider two techniques to divide location fingerprints into smaller clusters. Separate proximity graphs for each cluster are now used to predict performance and eliminate unnecessary location fingerprints. Results show that the computational effort can be reduced, creating a more scalable analytical model, while still predicting and enabling good precision performance. <s> BIB001 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Fingerprint Data Collection <s> With increasing user demands on Location-based Services (LBS) and Social Networking Services (SNS), indoor positioning has become more crucial. Because of the general failure of GPS indoors, non-GNSS navigation technologies are essential for such areas. Wireless Local Area Networks (WLAN) have widely been employed for indoor localisation based on the Received Signal Strength (RSS)-based location fingerprinting technique. The fingerprinting technique stores the location-dependent characteristics of a signal collected at known locations ahead of the system's use for localisation in a database. When positioning, the user's device records its own vector(s) of signal strength and matches it against the pre-recorded database of vectors by applying pattern matching algorithms. Location is then calculated based on the best matches between the new and stored vectors. We examined the relationship between the measured Manhattan Distance (MD), Euclidean Distance (ED), and other vector distances over the geometric distance between Reference Points (RPs) in a fingerprint database. The correlation between geometric and vector distance was poor. However, because “nearest neighbor” algorithms are used, only short vector distances are important. Furthermore, the measured RSSs varied much more as a function of distance (due to fast fading) than it did as a function of time at a single test point. Hence, the difference between variances measured at two test points was not a good indicator of the measured difference in signal strength. This led to the current investigation of very short geometric distances. In this paper, a new algorithm is applied to examine data from locations at very short ranges from each other and to investigate the relationship between vector distance and the geometric distance in closer areas in order to observe the nature of the relationship between short-range fingerprints. The experimental test bed was carried out in a large furnished office. Two west-east and south-north lines in a cross shape with 4m length are considered. We find that even at short distances, variation due to fading dominates and using other vector distances instead of MD or ED can help decrease the effect of such variation in positioning. <s> BIB002
Fingerprint data collection, also called offline training, is the first phase of fingerprint positioning, which uses the fingerprinting technique to store the location dependent characteristics of a signal collected at known locations ahead of the system's use for localization in a database BIB002 . The main task in this phase is to collect fingerprint data to build a model. There are two main methods of collecting fingerprint data, one is accurate modeling and the other is empirical modeling BIB001 .