The rapid growth in the technological advancements of the smartphone industry has classified contemporary smartphones as a low-cost and high quality indoor positioning tools requiring no additional infrastructure or equipment. In recent years, the fine time measurement (FTM) protocol, achieved through the Wi-Fi round trip time (RTT) observable, available in the most recent models, has gained the interest of many research teams worldwide, especially those concerned with indoor localization problems. However, as the Wi-Fi RTT technology is still new, there is a limited number of studies addressing its potential and limitations relative to the positioning problem. This paper presents an investigation and performance evaluation of Wi-Fi RTT capability with a focus on range quality assessment. A set of experimental tests was carried out, considering 1D and 2D space, operating different smartphone devices at various operational settings and observation conditions. Furthermore, in order to address device-dependent and other type of biases in the raw ranges, alternative correction models were developed and tested. The obtained results indicate that Wi-Fi RTT is a promising technology capable of achieving a meter-level accuracy for ranges both in line-of-sight (LOS) and non-line-of-sight (NLOS) conditions, subject to suitable corrections identification and adaptation. From 1D ranging tests, an average mean absolute error (MAE) of 0.85 m and 1.24 m is achieved, for LOS and NLOS conditions, respectively, for 80% of the validation sample data. In 2D-space ranging tests, an average root mean square error (RMSE) of 1.1m is accomplished across the different devices. Furthermore, the analysis has shown that the selection of the bandwidth and the initiator–responder pair are crucial for the correction model selection, whilst knowledge of the type of operating environment (LOS and/or NLOS) can further contribute to Wi-Fi RTT range performance enhancement.

Mining and quarrying industry has recently made a shift towards underground exploitation as a viable alternative to traditional open-pit approaches. Thus, emerged the imperative need for localization systems for personnel safety and operations’ monitoring purposes. While there are many approaches taking advantage of the various signals of opportunity (SoO) supported by Internet of Things (IoT) for indoor and underground navigation, the need for a GNSS alternative in such areas is still present in terms of meeting system and user requirements (scale, cost, availability, accuracy, and integrity). The goal of this research is to provide insights regarding different Radio Frequency (RF) technologies operation and evaluate their positioning capabilities (Wi-Fi, BLE and LoRa) in underground industrial facilities such as quarries and mines, following and expanding the tests of previous studies in controlled environment. Furthermore, the multi-sensory approach that this study is pursuing, aims to provide the foundations of a low-cost, scalable and robust positioning system. This system would integrate the characteristics of the aforementioned technologies in order to meet the application-specific user requirements and set the basis for a more efficient mobile mapping system. In this context, technologies’ characterization and comparison is presented, by using data from a real operating underground quarry. The data gathered lead to the conceptualization of the localization scheme, which besides the SoO observables, utilizes their availability status as an additional feature within the quarry as well. The proposed combined approach outperformed the rest, achieving an accuracy bellow 15m for the 85% of the test data, which is sufficient for typical quarrying operations monitoring and management requirements.

This study explores the potential of satellite signals L5, E5a and B2a tracked by contemporary Android smartphones. Particularly, the objective is to investigate their performance capabilities and vulnerabilities concerned with L1, E1 and B1 bandwidths and a focus on land vehicle ITS (Intelligent Transportation Systems) applications aiming to address low to medium PVT (Positioning, Velocity and Timing) solutions. In this regard raw, kinematic GNSS measurements from two Android smartphones were collected (Xiaomi Mi 8 and One Plus Nord 2 5G) under GNSS-harsh environments. The Single Point Positioning (SPP) technique was adopted for processing the single-frequency, multi-constellation raw GNSS measurements through an Extended Kalman Filter (EKF). The results obtained indicate the benefits and difficulties of exploiting modernized GNSS signals for road ITS applications.