Fingerprinting using Bluetooth Low Energy (BLE) has emerged as a promising positioning approach due to its technological flexibility and lack of limitations. However, Received Signal Strength (RSS) measurements in BLE exhibit higher variability partly attributed to the protocol’s utilization of different frequency channels within its range. In this study, we leverage frequency information to enhance positioning accuracy. We compare various methods adapted from the existing literature and propose novel approaches. Two distinct BLE RSS databases are employed, including one specifically developed for this research. The results demonstrate that utilizing frequency information can effectively improve positioning accuracy by 10% to 20%, regardless of the selected fingerprinting algorithm. However, the optimal combination method depends on the specific environmental conditions.

It is undeniable that more and more tasks in which drones work autonomously are becoming essential. Of particular importance are indoor human-transited environments. These entail a challenge for drone flights as the safety of people must be ensured at all times while they are in the vicinity of flights. Millimetre-wave radar has proven to be a technology that provides accurate position and velocity measurements, making it ideal for monitoring spaces in search of moving targets. Thus, this work proposes a security system based on millimetre-wave radar, using a processing workflow based on machine learning techniques to detect humans and interrupt drone flights until people are in a safe place. The feasibility of the system is demonstrated experimentally, with accuracy, precision, recall and F1 score greater than 99% and a real-time system performance video.

Synthetic data of high quality can provide research teams with an effective means of conducting large-scale evaluations of their indoor positioning systems under controlled conditions, while avoiding the significant effort and costs associated with real-world experiments and data collection/labelling. Moreover, it facilitates the fair comparison with other solutions, since data can be generated for more diverse conditions and can be shared without concerns. The work described in this paper aims to improve the quality of WiFi synthetic data by integrating new models for channel noise and beacon receive probability into the Dioptra tool. We compare the results of 13 fingerprinting methods used on 15 synthetic databases and 14 real-world databases. The results indicate that synthetic data can be an effective alternative/complement for the evaluation and comparison of WiFi-based positioning methods.

Bluetooth Low Energy (BLE) fingerprinting has gained a lot of research effort in recent years due to flexibility in both beacons placement and configuration. Different works have addressed the effect of the configuration parameters, mainly the transmission power (Tx) and period (Ts), over positioning accuracy but not on the system lifespan and the trade-off between these two. In this work, different configurations of one, three and six slots have been tested over the same experimental setup. Positioning accuracy was obtained using different variations of the Weighted k-Nearest Neighbours (Wk-NN) algorithm, and the system lifespan was estimated using the actual current consumption and transmission mechanism for each configuration. Experimental results have shown that Tx and the number of slots can be adjusted to optimize this trade-off; meanwhile, changes in Ts worsen Wk-NN results more than in the other parameters, showing that the minimum Ts is always the best option.

In radio-frequency Indoor Positioning Systems, the fingerprinting technique, which involves a laborious database construction stage to achieve good positioning results, has become a standard solution. Several researchers have tried to find methods to reduce the number of reference points – which need to be manually collected – while maintaining the positioning results, but have focused on other technologies, such as XBee or WLAN, and more specifically Wi-Fi. In this work, a comparison between different fingerprint generation techniques is made in a Bluetooth Low-Energy (BLE) Indoor Positioning System using Received Signal Strength Indicator measurements. Various databases are collected using three Android phones. These data are employed to train different methods that will generate fingerprints to reconstruct the missing points in the databases. The methods used are Inverse Distance Weighting, Support Vector Regression, Gaussian Process Regression and Generative Adversarial Networks. After this comparison, it has been found out that all these techniques could reconstruct part of the missing points and improve the positioning results. However, Generative Adversarial Networks achieved the best reconstruction throughout all of the databases.

Fingerprint-based indoor positioning is widely used in many contexts, including pedestrian and autonomous vehicles navigation. Many approaches have used traditional Machine Learning models to deal with fingerprinting, being k-NN the most common used one. However, the reference data (or radio map) is generally limited, as data collection is a very demanding task, which degrades overall accuracy. In this work, we propose a novel approach to add random noise to the radio map which will be used in combination with an ensemble model. Instead of augmenting the radio map, we create n noisy versions of the same size, i.e. our proposed Indoor Positioning model will combine n estimations obtained by independent estimators built with the n noisy radio maps. The empirical results have shown that our proposed approach improves the baseline method results in around 10% on average.