Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Accurate Modeling <s> Qualitative models are presented that predict the effects of walls, office partitions, floors, and building layout on the path loss at 914 MHz. The site-specific models have been developed based on the number of floors, partitions, and concrete walls between the transmitter and receiver, and provide simple prediction rules which relate signal strength to the log of distance. The standard deviation between measured and predicted path loss is 5.8 dB for the entire data set, and can be as small as 4 dB for specific areas within a building. Average floor attenuation factors, which describe the additional path loss (in decibels) caused by floors between transmitter and receiver, are found for as many as four floors in a typical office building. Path loss contour plots for measured data are presented. In addition, contour plots for the path loss prediction error indicate that the prediction models presented are accurate to within 6 dB for a majority of locations in a building. > <s> BIB001 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Accurate Modeling <s> The proliferation of mobile computing devices and local-area wireless networks has fostered a growing interest in location-aware systems and services. In this paper we present RADAR, a radio-frequency (RF)-based system for locating and tracking users inside buildings. RADAR operates by recording and processing signal strength information at multiple base stations positioned to provide overlapping coverage in the area of interest. It combines empirical measurements with signal propagation modeling to determine user location and thereby enable location-aware services and applications. We present experimental results that demonstrate the ability of RADAR to estimate user location with a high degree of accuracy. <s> BIB002 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Accurate Modeling <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> BIB003 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Accurate Modeling <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> BIB004 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Accurate Modeling <s> An indoor positioning system that uses a location fingerprinting technique based on the received signal strength of a wireless local area network is an enabler for indoor location-aware computing. Data analysis of the received signal strength indication is very essential for understanding the underlying location-dependent features and patterns of location fingerprints. This knowledge can assist a system designer in accurately modeling a positioning system, improving positioning performance, and efficiently designing such a system. This study investigates extensively through measurements, the features of the received signal strength indication reported by IEEE 802.11b/g wireless network interface cards. The results of the statistical data analysis help in identifying a number of phenomena that affect the precision and accuracy of indoor positioning systems. <s> BIB005 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Accurate Modeling <s> Having a wireless communication, indoor environment, in particular, represents unfriendly environment. Wireless sensor network applications indoors are in contrast to the mentioned fact and they are very common. Considering radio frequency signal propagation in a real environment (without bearing in mind interferences from other sources) we meet, in general, four basic phenomenon - reflection, path-loss, scattering and diffraction. Every single one impacts on the signal propagation and contributes to the path-loss and distortion at the receiver. A thorough picture of the electromagnetic wave propagation in the space can theoretically obtain the solution of Maxwell's equations. Though, this approach is computationally demanding and ineffective for the practical cases. In order to better describe the propagation of a radio frequency signal, the approximate models are used that usually base on experimental results. This paper presents case study on the indoor radio frequency signal propagation at 2.4 GHz ISM Band (Industrial, Scientific and Medical) supported by the propagation models implementation and experimental results.. <s> BIB006
Accurate modeling-also called channel propagation model modeling-only needs the signal strength of several important positions to generate the entire radio map by calculating and expanding the data with the channel propagation model. Accurate modeling requires the accurate position of the APs; however, the position can be hard to determine in most cases. Thus, channel propagation modeling can be used in an experimental environment where the position of the AP is known to find the relationships between fingerprint data BIB004 , or examine the performance of the proposed method. Furthermore, this modeling can be used in a real positioning environment to calibrate the positioning results obtained by other means. For radio frequency signals, signal propagation is always affected by scattering, reflection, and refraction. Due to the complexity and limitations of the indoor environment, this effect can be amplified, making the signal transmitted from the broadcaster to the receiver over a variety of paths. Called the multipath effect, it causes signal fading and phase shift BIB002 , which decreases the signal-to-noise ratio. Inaccurate signals can reduce the accuracy of positioning and bring larger distance errors. Therefore, the main task of accurate modeling is to establish a channel propagation model which can describe indoor signal fading properly. (1) Typical Channel Propagation Models Due to the complexity and limitation of the indoor environment and the large number of obstacles, there are four factors to be considered when building a channel propagation model: path decline, scattering, refraction, and reflection BIB006 BIB005 . As such, the channel propagation model should be combined with a large-scale fading model and a small-scale fading model BIB001 . Here, we introduce several typical channel propagation models BIB003 . Indoor General Propagation Model: In Equation (2), PL represents the path loss in the range of 1 m in free space, while Nsf represents the dissipation factor of the same layer, and FAF represents the additive value of the loss through different paths. The model does not take the obstacles in actual interior space into account, and one has to measure the actual field strength to acquire the value of Nsf and FAF before using this model. Chan Propagation Model: In Equation (3), L fs represents the pass loss in free space and L p represents the loss value associated with the interior walls. The Chan propagation model is the most basic prediction model of indoor path loss, and it takes the impact of indoor walls on channel propagation into account based on the free space propagation model. Loss Factor Propagation Model: In Equation (4), PL represents the path loss in the range of 1 m in free space; η sf represents the testing index of area to be tested; and FAF represents the additive value of the loss through different paths. Motely Propagation Model: In Equation (5), PL 0 represents the path loss in the range of 1 m in free space, while n represents loss factor (value recommended as 2 in this model). d represents the distance between wireless devices; F wall represents the correction factor of the signal attenuation caused by walls; and F floor represents the correction factor of the signal attenuation caused by floor. Both correction factors change with materials.