Many infrastructure-free indoor positioning systems rely on fine-grained location-dependent fingerprints to train models for localization. The site survey process to collect fingerprints is laborious and is considered one of the major obstacles to deploying such systems. In this paper, we propose TuRF, a fast path-based fingerprint collection mechanism for site survey. We demonstrate the feasibility to collect fingerprints for indoor localization during walking along predefined paths. A step counter is utilized to accommodate the variations in walking speed. Approximate location labels inferred from the steps are then used to train a Gaussian Process regression model. Extensive experiments show that TuRF can significantly reduce the required time for site survey, without compromising the localization performance.
In fingerprint-based IPS, although all location-dependent environmental measures can be utilized as fingerprints, magnetic field magnitudes and WiFi RSSes are most often used. Traditionally, magnetic field measurements are used for heading estimation. Magnetic field anomalies caused by building materials and magnetic interference from machinery and IT equipment make them unsuitable for heading but attractive for fingerprinting or landmark identification @cite_13 @cite_9 . Special devices have been developed to utilize magnetic field for small range indoor navigation @cite_9 . In general, magnetic field vectors are not unique in a large area, but can be used to differentiate different locations in a small area. As a result, recent studies @cite_13 @cite_14 combine WiFi RSSes and magnetic field measurements for indoor localization. Moreover, magnetic field readings have been shown to reduce the searching space in fingerprint based indoor localization @cite_14 .
@cite_9 @cite_14 @cite_13
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The Received Signal Strength (RSS) based fingerprinting approaches for indoor localization pose a need for updating the fingerprint databases due to dynamic nature of the indoor environment. This process is hectic and time-consuming when the size of the indoor area is large. The semi-supervised approaches reduce this workload and achieve good accuracy around 15 percent of the fingerprinting load but the performance is severely degraded if it is reduced below this level. We propose an indoor localization framework that uses unsupervised manifold alignment. It requires only 1 percent of the fingerprinting load, some crowd sourced readings, and plan coordinates of the indoor area. The 1 percent fingerprinting load is used only in perturbing the local geometries of the plan coordinates. The proposed framework achieves less than 5 m mean localization error, which is considerably better than semi-supervised approaches at very small amount of fingerprinting load. In addition, the few location estimations together with few fingerprints help to estimate the complete radio map of the indoor environment. The estimation of radio map does not demand extra workload rather it employs the already available information from the proposed indoor localization framework. The testing results for radio map estimation show almost 50 percent performance improvement by using this information as compared to using only fingerprints. Indoor localization is of great importance for a range of pervasive applications, attracting many research efforts in the past decades. Most radio-based solutions require a process of site survey, in which radio signatures of an interested area are annotated with their real recorded locations. Site survey involves intensive costs on manpower and time, limiting the applicable buildings of wireless localization worldwide. In this study, we investigate novel sensors integrated in modern mobile phones and leverage user motions to construct the radio map of a floor plan, which is previously obtained only by site survey. Considering user movements in a building, originally separated RSS fingerprints are geographically connected by user moving paths of locations where they are recorded, and they consequently form a high dimension fingerprint space, in which the distances among fingerprints are preserved. The fingerprint space is then automatically mapped to the floor plan in a stress-free form, which results in fingerprints labeled with physical locations. On this basis, we design LiFS, an indoor localization system based on off-the-shelf WiFi infrastructure and mobile phones. LiFS is deployed in an office building covering over 1,600 m @math , and its deployment is easy and rapid since little human intervention is needed. In LiFS, the calibration of fingerprints is crowdsourced and automatic. Experiment results show that LiFS achieves comparable location accuracy to previous approaches even without site survey. Fingerprinting techniques, which are a common method for indoor localization, have been recently applied with success into outdoor settings. Particularly, the communication signals of Low Power Wide Area Networks (LPWAN) such as Sigfox, have been used for localization. In this rather recent field of study, not many publicly available datasets, which would facilitate the consistent comparison of different positioning systems, exist so far. In the current study, a published dataset of RSSI measurements on a Sigfox network deployed in Antwerp, Belgium is used to analyse the appropriate selection of preprocessing steps and to tune the hyperparameters of a kNN fingerprinting method. Initially, the tuning of hyperparameter k for a variety of distance metrics, and the selection of efficient data transformation schemes, proposed by relevant works, is presented. In addition, accuracy improvements are achieved in this study, by a detailed examination of the appropriate adjustment of the parameters of the data transformation schemes tested, and of the handling of out of range values. With the appropriate tuning of these factors, the achieved mean localization error was 298 meters, and the median error was 109 meters. To facilitate the reproducibility of tests and comparability of results, the code and train validation test split used in this study are available. Indoor positioning systems have received increasing attention for supporting location-based services in indoor environments. WiFi-based indoor localization has been attractive due to its open access and low cost properties. However, the distance estimation based on received signal strength indicator (RSSI) is easily affected by the temporal and spatial variance due to the multipath effect, which contributes to most of the estimation errors in current systems. How to eliminate such effect so as to enhance the indoor localization performance is a big challenge. In this work, we analyze this effect across the physical layer and account for the undesirable RSSI readings being reported. We explore the frequency diversity of the subcarriers in OFDM systems and propose a novel approach called FILA, which leverages the channel state information (CSI) to alleviate multipath effect at the receiver. We implement the FILA system on commercial 802.11 NICs, and then evaluate its performance in different typical indoor scenarios. The experimental results show that the accuracy and latency of distance calculation can be significantly enhanced by using CSI. Moreover, FILA can significantly improve the localization accuracy compared with the corresponding RSSI approach.