Abstract
Due to recent advances in wireless gadgets and mobile computing, the location-based services have attracted the attention of computing and telecommunication industries to launch location-based fast and accurate localization systems for tracking, monitoring and navigation. Traditional lateration-based techniques have limitations, such as localization error, and modeling of distance estimates from received signals. Fingerprinting based tracking solutions are also environment dependent. On the other side, machine learning-based techniques are currently attracting industries for developing tracking applications. In this paper we have modeled a machine learning method known as Linear Discriminant Analysis (LDA) for real time dynamic object localization. The experimental results are based on real time trajectories, which validated the effectiveness of our proposed system in terms of accuracy compared to naive Bayes, k-nearest neighbors, a support vector machine and a decision tree.
Highlights
Due to the latest advancements in wireless technology, demands for location-based mobile applications, and hardware solutions for tracking and localization have increased
In order to address this problem, we propose a method based on linear discriminant analysis (LDA) for the tracking and position estimation of a dynamic object in an indoor environment using the Bluetooth Low Energy modules
This paper presented a comparative analysis of different machine learning classifiers for real-time object localization and tracking in an indoor environment
Summary
Due to the latest advancements in wireless technology, demands for location-based mobile applications, and hardware solutions for tracking and localization have increased. The distance estimation is used in trilateration, MinMax or least square based position estimation techniques to compute the actual location of the user Various methods, such as particle filters, Kalman filters and extended Kalman filters have been used for object localization using RSSI distance modeling. There are some hybrid solutions that combine distance-based localization techniques with the fingerprinting techniques to improve the position estimation accuracy [6,7] These methods may improve localization accuracy at one location, but due to fluctuations in transmission power, especially in the case of Bluetooth, accurate position estimation is still a challenging task [8]. In order to address this problem, we propose a method based on linear discriminant analysis (LDA) for the tracking and position estimation of a dynamic object in an indoor environment using the Bluetooth Low Energy modules.
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