Map-aided fingerprint-based indoor positioning (original) (raw)
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Multi-device Map-aided Fingerprint-based Indoor Positioning using Ray Tracing
2017
The objective of this work is to investigate potential accuracy improvements in the fingerprint–based indoor positioning processes, by imposing map-constraints into the positioning algorithms in the form of a–priori knowledge. In our approach, we propose the introduction of a Route Probability Factor (RPF), which reflects the possibility of a user, to be located on one position instead of all others. The RPF does not only affect the probabilities of the points along the pre-defined frequent routes, but also influences all the neighbouring points that lie at the proximity of each frequent route. The outcome of the evaluation process, indicates the validity of the RPF approach, demonstrated by the significant reduction of the positioning error.
Optimizing route prior knowledge for map-aided fingerprint-based positioning systems
The 8th European Conference on Antennas and Propagation (EuCAP 2014), 2014
This paper investigates how positioning accuracy is affected in map-aided positioning systems, when a user's typical route is described by different probability distribution types. Probability distributions are introduced in an effort to better explain any reasonable route deviations from the user's center line of movement. The user route is assumed to be apriori knowledge. Such knowledge can be extracted by utilizing information from environment maps and user mobility behaviour within the area of interest. In our research work, several probability distributions are tested along the center line of a user's route. The effect of the distribution width, radius ρ, on positioning accuracy is also investigated, by varying the value of ρ for both sides of the route. In this way, the allocated weight probability for locations at the proximity of the user's center line route can be controlled. Results suggest that even simple probability distributions outperform the positioning accuracy of the scenarios where no map-aided positioning method is used. Significant accuracy improvement is also expected when the distance ratio probability distribution is utilized.
Location Fixing and Fingerprint Matching Fingerprint Map Construction for Indoor Localization
Journal of Sensors
Building the fingerprint map for indoor localization problems is a labour-intensive and time-consuming process. However, due to its direct influence on the location estimation accuracy, finding a proper mechanism to construct the fingerprint map is essential to enhance the position estimation accuracy. Therefore, in this work, we present a fingerprint map construction technique based on location fix determination and fingerprint matching motivated by the availability of advanced sensing capabilities in smartphones to reduce the time and labour cost required for the site survey. The proposed Location Fixing and Finger Matching (LFFM) method use a landmark graph-based localization approach to automatically estimate the location fixes for the Reference Points and matching the collected fingerprints, without requiring active user participation. Experimental results show that the proposed LFFM is faster than the manual fingerprint map construction method and remarkably improves the posit...
The Novel Performance Evaluation Method of the Fingerprinting-Based Indoor Positioning
IEICE Transactions on Information and Systems, 2016
In this work, the novel fingerprinting evaluation parameter, which is called the punishment cost, is proposed. This parameter can be calculated from the designed matrix, the punishment matrix, and the confusion matrix. The punishment cost can describe how well the result of positioning is in the designated grid or not, by which the conventional parameter, the accuracy, cannot describe. The experiment is done with real measured data on weekdays and weekends. The results are considered in terms of accuracy and the punishment cost. Three well-known machine learning algorithms, i.e. Decision Tree, k-Nearest Neighbors, and Artificial Neural Network, are verified in fingerprinting positioning. In experimental environment, Decision Tree can perform well on the data from weekends whereas the performance is underrated on the data from weekdays. The k-Nearest Neighbors has proper punishment costs, even though it has lower accuracy than that of Artificial Neural Network, which has moderate accuracies but lower punishment costs. Therefore, other criteria should be considered in order to select the algorithm for indoor positioning. In addition, punishment cost can facilitate the conversion spot positioning to floor positioning without data modification.
Scalable and Efficient Clustering for Fingerprint-Based Positioning
IEEE Internet of Things Journal
Indoor positioning based on IEEE 802.11 wireless LAN (Wi-Fi) fingerprinting needs a reference data set, also known as a radio map, in order to match the incoming fingerprint in the operational phase with the most similar fingerprint in the data set and then estimate the device position indoors. Scalability problems may arise when the radio map is large, e.g., providing positioning in large geographical areas or involving crowdsourced data collection. Some researchers divide the radio map into smaller independent clusters, such that the search area is reduced to less dense groups than the initial database with similar features. Thus, the computational load in the operational stage is reduced both at the user devices and on servers. Nevertheless, the clustering models are machine-learning algorithms without specific domain knowledge on indoor positioning or signal propagation. This work proposes several clustering variants to optimize the coarse and fine-grained search and evaluates them over different clustering models and data sets. Moreover, we provide guidelines to obtain efficient and accurate positioning depending on the data set features. Finally, we show that the proposed new clustering variants reduce the execution time by half and the positioning error by ≈ 7% with respect to fingerprinting with the traditional clustering models.
A Multimodal Fingerprint-Based Indoor Positioning System for Airports
IEEE Access, 2018
Indoor localization techniques are becoming popular in order to provide a seamless indoor positioning system enhancing the traditional GPS service that is only suitable for outdoor environments. Though there are proprietary and costly approaches targeting high accuracy positioning, Wi-Fi and BLE networks are widely deployed in many public and private buildings (e.g. shopping malls, airports, universities, etc.). These networks are accessible through mobile phones resulting in an effective commercial offthe-self basic infrastructure for an indoor service. The obtained positioning accuracy is still being improved and there is ongoing research on algorithms adapted for Wi-Fi and BLE and also for the particularities of indoor environments. This paper focuses not only on indoor positioning techniques, but also on a multimodal approach. Traditional proposals employ only one network technology whereas this paper integrates two different technologies in order to provide improved accuracy. It also sets the basis for combining (merging) additional technologies, if available. The initial results show that the positioning service performs better with a multimodal approach compared to individual (monomodal) approaches and even compared with Google's geolocation service in public spaces such as airports. INDEX TERMS BLE beacons, indoor location, indoor positioning, Internet of Things, Wi-Fi fingerprinting.
Using unlocated fingerprints in generation of WLAN maps for indoor positioning
Record - IEEE PLANS, Position Location and Navigation Symposium, 2012
This paper presents five methods for generation of WLAN maps for indoor positioning using crowdsourced fingerprints. A fingerprint is assumed to contain identifiers of WLAN access points, received signal strength values and, if the fingerprint is collected outdoors, a GPS position. The proposed methods use the fingerprints' information to generate a WLAN map that contains estimated access point locations. Two of the proposed methods use RSS values in access point location estimation. In our evaluation with simulations and with real data, the Access Point Least Squares method, which does not use RSS information, is the fastest and its accuracy is as good as more complex methods that use RSS information.
Indoor Positioning Using the Modified Fingerprint Technique
2013
The Wi-Fi positioning systems available for enclosed spaces use the existing network infrastructure to calculate the position of the mobile device (MD). The most commonly used parameter is RSSI (Received Signal Strength Indicator). In this paper, we analyze the Fingerprint technique considering some variations aimed at improving the accuracy of the technique and minimizing calculation time. Significant field work is carried out, analyzing the accuracy achieved with each technique.
Mobile Information Systems, 2016
Fingerprinting-based algorithms are popular in indoor location systems based on mobile devices. Comparing the RSSI (Received Signal Strength Indicator) from different radio wave transmitters, such as Wi-Fi access points, with prerecorded fingerprints from located points (using different artificial intelligence algorithms), fingerprinting-based systems can locate unknown points with a few meters resolution. However, training the system with already located fingerprints tends to be an expensive task both in time and in resources, especially if large areas are to be considered. Moreover, the decision algorithms tend to be of high memory and CPU consuming in such cases and so does the required time for obtaining the estimated location for a new fingerprint. In this paper, we study, propose, and validate a way to select the locations for the training fingerprints which reduces the amount of required points while improving the accuracy of the algorithms when locating points at room level ...
Indoor Localization Accuracy Estimation from Fingerprint Data
2017 18th IEEE International Conference on Mobile Data Management (MDM), 2017
The demand for indoor localization services has led to the development of techniques that create a Fingerprint Map (FM) of sensor signals (e.g., magnetic, Wi-Fi, bluetooth) at designated positions in an indoor space and then use FM as a reference for subsequent localization tasks. With such an approach, it is crucial to assess the quality of the FM before deployment, in a manner disregarding data origin and at any location of interest, so as to provide deployment staff with the information on the quality of localization. Even though FM-based localization algorithms usually provide accuracy estimates during system operation (e.g., visualized as uncertainty circle or ellipse around the user location), they do not provide any information about the expected accuracy before the actual deployment of the localization service. In this paper, we develop a novel framework for quality assessment on arbitrary FMs coined ACCES. Our framework comprises a generic interpolation method using Gaussian Processes (GP), upon which a navigability score at any location is derived using the Cramer-Rao Lower Bound (CRLB). Our approach does not rely on the underlying physical model of the fingerprint data. Our extensive experimental study with magnetic FMs, comparing empirical localization accuracy against derived bounds, demonstrates that the navigability score closely matches the accuracy variations users experience.