Multi-device Map-aided Fingerprint-based Indoor Positioning using Ray Tracing (original) (raw)
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Map-aided fingerprint-based indoor positioning
2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), 2013
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.
Multidevice Map-Constrained Fingerprint-Based Indoor Positioning Using 3-D Ray Tracing
IEEE Transactions on Instrumentation and Measurement, 2018
This paper studies the use of deterministic channel modelling through 3D Ray Tracing (RT) for constructing deviceindependent radiomaps for Wi-Fi RSSI-based fingerprinting indoor positioning, applicable to different devices. Device heterogeneity constitutes a limitation in fingerprint-based approaches and also constructing radiomaps through extensive in-situ measurement campaigns is laborious and time-consuming even with a single device let alone the need for radiomaps constructed using multiple different devices. This work tackles both challenges through the use of 3D RT for radiomap generation in conjunction with data calibration using a small set of device-specific measurements to make the radiomap device-independent. The efficiency of this approach is evaluated using simulations and measurements in terms of the time spent to generate the radiomap, the amount of device-specific data required for calibration and in terms of the achievable positioning accuracy. Potential accuracy improvements in the RT-based indoor positioning processes are further investigated, by studying the use of map constraints into the algorithm in the form of a-priori probabilities. In this approach, a Route Probability Factor (RPF), which reflects the likelihood of a user being in various locations inside the environment is used. The outcome of the evaluation process which includes a study of different RPF distributions, indicates the validity of the approach, demonstrated by a reduction in the positioning error for various devices. The versatility of this approach is also demonstrated for different scenarios, different devices and by considering different device-handling conditions.
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...
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.
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.
Remote Sensing
Numerous indoor positioning technologies and systems have been proposed to localize people and objects in large buildings. Wi-Fi and Bluetooth positioning systems using fingerprinting have gained popularity, due to the wide availability of existing infrastructure. Unfortunately, the implementation of fingerprinting-based methods requires time-consuming radio surveys to prepare databases (RSSI maps) that serve as a reference for the radio signal. These surveys must be conducted for each individual building. Here, we investigate the possibility of using simulated RSSI maps with fingerprinting-based indoor localization systems. We discuss the suitability of the two popular radio wave propagation models for the preparation of RSSI reference data: ray tracing and multiwall. Based on an analysis of several representative indoor scenarios, we evaluated the performance of RSSI distribution maps obtained from simulations versus maps obtained from measurement campaigns. An experimental positi...
Accurate Indoor-Positioning Model Based on People Effect and Ray-Tracing Propagation
Sensors
Wireless local area networks (WLAN)-fingerprinting has been highlighted as the preferred technology for indoor positioning due to its accurate positioning and minimal infrastructure cost. However, its accuracy is highly influenced by obstacles that cause fluctuation in the signal strength. Many researchers have modeled static obstacles such as walls and ceilings, but few studies have modeled the people’s presence effect (PPE), although the human body has a great impact on signal strength. Therefore, PPE must be addressed to obtain accurate positioning results. Previous research has proposed a model to address this issue, but these studies only considered the direct path signal between the transmitter and the receiver whereas multipath effects such as reflection also have a significant influence on indoor signal propagation. This research proposes an accurate indoor-positioning model by considering people’s presence and multipath using ray-tracing, we call it (AIRY). This study propo...
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 ...
Multi Fingerprint Map for Indoor Localisation
2015
Fingerprinting is one of the location estimation technique used in indoor applications. It maps information about wireless signals (e.g. the RSS value) into spatial coordinates. Because WiFi is an ubiquitous communication technology, supported by smartphones, Fingerprinting-based localisation algorithms that use WiFi signals are suitable for LBS applications. Although good results can be achieved using Fingerprinting, this is not an error free localisation technique. The end-user of the LBS application can interfere with these algorithms. If a user that was facing an Access Point rotates 180, the received RSS from that Access Point will decrease (and vice-versa). Although the user did not move, this RSS variation might be interpreted as ”the user moved”. A possible solution to cope with this problem is to acquire data at different directions, at each spatial point, during the off-line phase. Multiple Fingerprint Maps, that also include direction information, can therefore be built. ...