Abstract
Indoor Positioning Systems (IPSs) are used to estimate the position of mobile devices in indoor environments. Fingerprinting is the most used technique because of its higher accuracy. However, this technique requires a labor-intensive training phase that measures the Received Signal Strength Indicator (RSSI) at all Reference Points (RPs) locations. On the other hand, model-based IPSs use signal propagation models to estimate distances from RSSI. Thus, they do not require expensive training but result in higher positioning errors. In this work, we propose SynTra-IPS (Synthetic Training Indoor Positioning System), a hybrid approach between a fingerprint and a model-based IPS that uses synthetic, simulated datasets combined with data fusion techniques to eliminate the fingerprint collection cost. In our solution, we use the map of the scenario, with known anchor nodes’ positions and the log-distance signal propagation model, to generate several synthetic, model-based, fingerprint training datasets. In the online phase of our solution, the positions estimated by the several synthetic datasets using K-Nearest Neighbors (KNN) are combined using data fusion techniques into a single, more accurate position. We evaluated the performance of our SynTra solution in a real-world, large-scale environment using mobile devices with Bluetooth Low Energy (BLE) technology, and we compared our solution to classic approaches from the literature. Our results show that SynTra can locate mobile devices with an average error of only 2.36 m while requiring no real-world environment training.
Highlights
Positioning systems can be defined as the process of finding the position of a target in outdoor or indoor environments [1]
Our results show that the system can achieve a competitive localization accuracy compared to stateof-the-art Indoor Positioning Systems (IPSs) such as model-based IPSs, IPSs using a single synthetic dataset, and even traditional fingerprint-based IPSs with real training
To reduce the effort of data collection, we propose and evaluate a new fingerprint-based IPS, that uses a signal propagation model to generate several synthetic training datasets
Summary
Positioning systems can be defined as the process of finding the position of a target in outdoor or indoor environments [1]. Indoor Positioning Systems (IPSs) focus on locating mobile devices in indoor environments, where GNSS can not provide a good accuracy [2]. The RSSI being the most frequently used due to its high availability since most devices with wireless communication, such as Bluetooth Low Energy (BLE) or WiFi, already comes with this feature. WiFi is a wireless communication technology widely available in different places such as malls and airports, which means no additional hardware and deployment requirements for indoor localization. BLE has been widely used in indoor localization due to its low power consumption, allowing it to be used by energy-constrained devices such as smartwatches while being available in most smartphones
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