Accurate localization is essential for autonomous driving and Advanced Driver Assistance Systems (ADAS) to ensure safe and reliable vehicle navigation. In urban environments, multipath propagation is a major source of error in GNSS-based positioning, as signals reflect off buildings, structures, and other city elements, leading to degraded localization performance. This issue significantly impacts autonomous vehicles and ADAS applications, where precise positioning is critical for decision-making and safety. To address this challenge, we propose a method to simulate the multipath effect using a ray-tracing approach based on the 3D city model. The CARLA open-source simulator is used to recreate urban environments and vehicle trajectories, while an octree-based ray-tracing technique computes the possible signal reflections from GNSS satellites to vehicle positions. These signals are then used to estimate the amplitude and phase of both Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) components, allowing for the calculation of multipath-induced errors using standard Delay Lock Loop (DLL) mechanisms. Finally, raw pseudorange and carrier phase measurements are generated, incorporating the estimated multipath effects. The proposed model provides a more detailed simulation of multipath propagation and signal obstructions compared to existing methods. This is reflected in the increased positioning error observed in urban scenarios, particularly in areas with limited satellite visibility, such as under rail tracks. To promote open science and reproducibility, we also share the dataset obtained with this simulation pipeline, containing the generated measurements as well as the results presented in this paper. This simulation approach has broad applications, including the development of enhanced localization algorithms for ADAS and autonomous vehicles, multipath mitigation techniques, sensor fusion strategies, and Artificial Intelligence-driven positioning systems. • Simulation framework for GNSS multipath using CARLA and raw sensor data. • Ray tracing to model multipath in dynamic urban driving scenarios. • Multipath interference modelling for accurate signal power, delay, and phase. • Flexible, low-cost GNSS multipath simulation for urban vehicle testing. • Public dataset with GNSS, IMU, and ground truth from 3D urban simulations.

Complex real-world environments, such as malls, airports, and hospitals, are adopting indoor positioning and navigation systems at a growing rate. Recent trends emphasize dynamic trajectory-based evaluation over static point assessments to reflect realistic user movement. The IPIN Competition exemplifies this shift, assessing positioning solutions in diverse settings using natural trajectories. Although 2D localization accuracy has improved, floor-level misclassification remains a critical issue, especially for users with accessibility needs. Real-world features, such as auditorium layouts or uneven floor transitions, can challenge system performance, as evidenced in the IPIN Competition over the last 10 years. This paper investigates the use of Wi-Fi fingerprinting to detect incorrect floor transitions during user movement. We assess feasibility across six scenarios and release annotated datasets from IPIN Competition tracks to support further research on floor detection under dynamic conditions. The results are promising, as floor detection rates over 95% can be reached in all scenarios, the final precision being dependent on the experimental setup and the complexity of the scenario.

The importance of reproducibility and transparency in scientific research has always been a cornerstone of the scientific Ethos. Recently, after identifying the challenges in terms of reproducibility in various research fields, the necessity of wide adoption of Open Science practices has become prominent. The field of Indoor Positioning and Indoor Navigation is no exception to these realizations. The current work provides a comprehensive review of Open Science practices followed in recent publications in the field, analyzing all reference papers from the 2022 and 2023 editions of the International Conference on Indoor Positioning and Indoor Navigation (IPIN). Particularly, the level of use of open data, open code, and open materials, is studied. Moreover, for all works relying on Open Research Data (ORD), our analysis went a step deeper characterizing multiple relevant features describing the data used, such as the technologies, the measurement types, and the environments associated with the open data. Our findings reveal that 22.4% of papers use open research data, 10.5% utilize open code, and 21.1% incorporate other open materials. However, only 7.9% of papers provide both open data and code. This study underscores the need for wider adoption of those practices, to enhance the transparency, reproducibility, replicability, and reliability of research outcomes of the field of indoor positioning. The files containing the complete characterization of the reviewed publications and of the Open Science practices followed are publicly available in [1].

In modern wireless networks evolving towards 6thgeneration, localization, and sensing in indoor environments play an increasingly critical role in ensuring reliability, security, and control over network users, including vehicular assets. Despite recent advancements in deep learning, using k-Nearest Neighbors (k-NN) as a positioning algorithm in Received Signal Strength Indicator (RSSI) fingerprinting-based localization still provides numerous advantages, including localization accuracy, reliability, and interpretability. In this work, we introduce Intersection over Union (IoU) as a novel similarity metric and introduce κ-enhanced k-NN, which enables dynamic neighbor selection leading to improved performance and generalization capabilities of the positioning algorithm. In the evaluation using 26 publicly available indoor positioning datasets, we clearly show the improvements in localization accuracy of the combined IoU with κ-enhanced k-NN over the relevant baselines.

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.

Wi-Fi-based positioning systems, particularly the ones based on Wi-Fi fingerprinting, rely on a Radio Map (RM) which represents the radio environment at the time when it was collected. Over time, phenomena such as the propagation effects or adding/removing Access Points (APs) from an indoor environment may lead to significant variations in the radio environment, thus leading to errors in estimated positions. Although it is common knowledge that RMs degrade over time, it is difficult to predict and detect when degradation causes large errors. In this paper, we propose a method that continuously monitors the radio environment and uses Radial Basis Functions (RBF) interpolation to automatically enrich an old RM with new information. Before enriching the RM, AP selection is performed to remove APs that disappeared and mobile APs from the old radio map. Then, the analysis of the radio environment is performed to select newly detected APs to enrich the radio map, based on predefined criteria. Our experiments with real-world data show a significant improvement over 100% in mean error when using the enriched RM. This approach presents a promising solution to overcome the RM degradation in Wi-Fi fingerprinting, with potential applications in indoor positioning and location-based services.

Microsoft proposed RADAR in 2000, the first indoor positioning system based on Wi-Fi fingerprinting. Since then, the indoor research community has worked not only to improve the base estimator but also on finding an optimal RSS data representation. The long-term objective is to find a positioning system that minimises the mean positioning error. Despite the relevant advances in the last 23 years, a disruptive solution has not been reached yet. The evaluation with non-open datasets and comparisons with non-optimized baselines make the analysis of the current status of fingerprinting for indoor positioning difficult. In addition, the lack of implementation details or data used for evaluation in several works make results reproducibility impossible. This paper focuses on providing a comprehensive analysis of fingerprinting with k-NN and settling the basement for replicability and reproducibility in further works, targeting to bring relevant information about k-NN when it is used as a baseline comparison of advanced fingerprint-based methods.

Localization and tracking of industrial vehicles have a key role in increasing productivity and improving the logistics processes of factories. Due to the demanding requirements of industrial vehicle tracking and navigation, existing systems explore technologies, such as LiDAR or ultra wide-band to achieve low positioning errors. In this article we propose TrackInFactory, a system that combines Wi-Fi with motion sensors, achieving submeter accuracy and a low maximum error. A tight coupling approach is explored in sensor fusion with a particle filter (PF). Information regarding the vehicle’s initial position and heading is not required. This approach uses the similarity of Wi-Fi samples to update the particles’ weights as they move according to motion sensor data. The PF dynamically adjusts its parameters based on a metric for estimating the confidence in position estimates, allowing to improve positioning performance. A series of simulations were performed to tune the PF. Then the approach was validated in real-world experiments with an industrial tow tractor, achieving a mean error of 0.81 m. In comparison to a loose coupling approach, this method reduced the maximum error by more than 60% and improved the overall mean error by more than 20%.

Indoor Positioning Systems (IPSs) based on different approaches and technologies have been proposed to support localization and navigation applications in indoor environments. The fair benchmarking and comparison of these IPSs is a difficult task since each IPS is usually evaluated in very specific and controlled conditions and using private data sets, not allowing reproducibility and direct comparison between the reported results and other competing solutions. In addition, testing and evaluating an IPS in the real world is difficult and time-consuming, especially when considering evaluation in multiple environments and conditions. To enhance IPS assessment, we propose Dioptra, an open access and user-friendly application to support research, development and evaluation of IPSs through simulation. To the best of our knowledge, Dioptra is the first application specially developed to generate synthetic datasets to promote reproducibility and fair benchmarking between IPSs.

One of the most common assumptions regarding indoor positioning systems based on Wi-Fi fingerprinting is that the Radio Map (RM) becomes outdated and has to be updated to maintain the positioning performance. It is known that propagation effects, the addition/removal of Access Points (APs), changes in the indoor layout, among others, cause RMs to become outdated. However, there is a lack of studies that show how the RM degrades over time. In this paper, we describe an empirical study, based on real-world experiments, to evaluate how and why RMs degrade over time. We conducted site surveys and deployed monitoring devices to analyse the radio environment of one building over 2+ years, which allowed us to identify significant changes/events that caused the degradation of RMs. To quantify the RM degradation, we use the positioning error and propose the RM degradation ratio, a metric to directly compare two RMs and measure how different they are. Obtained results show that the positioning performance is much better when RMs are collected on the same day as the test data, and although RM degradation tends to increase over time, it only leads to large positioning errors when significant changes occur in the Wi-Fi infrastructure, making previous RMs outdated.

The evaluation of Indoor Positioning Systems (IPSs) mostly relies on local deployments in the researchers' or partners' facilities. The complexity of preparing comprehensive experiments, collecting data, and considering multiple scenarios usually limits the evaluation area and, therefore, the assessment of the proposed systems. The requirements and features of controlled experiments cannot be generalized since the use of the same sensors or anchors density cannot be guaranteed. The dawn of datasets is pushing IPS evaluation to a similar level as machine-learning models, where new proposals are evaluated over many heterogeneous datasets. This paper proposes a way to evaluate IPSs in multiple scenarios, that is validated with three use cases. The results prove that the proposed aggregation of the evaluation metric values is a useful tool for high-level comparison of IPSs.

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.

FastGraph is a novel positioning approach recently proposed to address the challenges of positioning in large spaces. Wi-Fi-based indoor positioning solutions often require complex and time-consuming deployments. Fingerprinting, as one of most used approaches, relies on a radio map, usually created by manual site survey, a process unpractical even for small spaces. Moreover, the site survey has to be repeated frequently due to the changes in the radio environment. Wi-Fi-based solutions are also frequently discarded for applications such as indoor vehicle navigation due to limited accuracy. This article introduces FastGraph Enhanced, a new version of FastGraph, able to provide high accuracy positioning, opening new fields of application, such as navigation for autonomous vehicles. In FastGraph Enhanced, the 3D Force-Directed Graph-Based method, used to model the radio environment, is extended with new algorithms, allowing to improve, among other aspects, the positioning performance. The core advantages of FastGraph are maintained, not requiring previous calibration or knowledge about the space. The proposed solution was evaluated in real world, with very significative improvements in positioning accuracy when compared with the basic version of FastGraph (from around 5m to 0.5m), and with state-of-the-art solutions.

The interference in Wi-Fi networks severely degrades the quality of service. In contrast to cellular networks that operate in regulated radio frequencies, and where the use of the radio spectrum is controlled and planned, the deployment of Wireless LAN networks infrastructures is unregulated and often uncontrolled, leading to high levels of interference. The interference analysis process normally requires a manual site survey, similar to the site survey required by indoor positioning Fingerprinting, which in large buildings is unpractical.In this paper we argue that the recently introduced FastGraph solution [1], [2], in addition to provide unsupervised positioning, can also be used to automatically map the level of interference in Wi-Fi environments. The proposed solution can automatically provide interference maps based on two distinct strategies: AP-Based Interference Mapping or Sample-Based Interference Mapping, each one with its own characteristics and advantages.The experimental results presented in this paper suggest that FastGraph can be used to easily create interference maps. In addition, a good correlation between the level of interference and the positioning error was observed - to the best of our knowledge, a relationship never reported before. This finding is very interesting, and can be explored for example to improve the positioning performance, as well as to provide a confidence indicator for the estimated positions, which it is currently not possible to obtain in Wi-Fi Fingerprinting-based systems.

Wi-Fi fingerprinting is a popular technique for smartphone-based indoor positioning. However, well-known RF propagation issues create signal fluctuations that translate into large positioning errors. Large errors limit the usage of Wi-Fi fingerprinting in industrial environments, where the reliability of position estimates is a key requirement. One successful approach to deal with signal fluctuations is to average the signals collected simultaneously through independent Wi-Fi interfaces. Another successful approach is to average the estimates provided by models built on independent radio maps. This paper explores multiple combinations of both approaches and determines the procedure to select the best model based on them through a simulated environment. The evaluation of the proposed model in a real-world industrial scenario shows that the positioning error (according to different metrics including the 95thand 99thpercentiles) is highly improved with respect to the traditional fingerprint.

It is well-known that fingerprinting-based positioning requires an exhaustive calibration phase to create a radio map, which often requires recalibration. Model-based and geometric approaches try to mitigate this effort at the expense of a lower accuracy or high computational cost. This paper introduces FastGraph, where a 3D graph is used to rapidly model the radio propagation environment. By means of unsupervised techniques, FastGraph is able to operate shortly after its deployment without previous knowledge about the environment. The proposed solution uses a novel algorithm to automatically provide location while simultaneously updating the radio map; and learn the position of the Access Points (APs) and location-specific radio propagation parameters. FastGraph has been evaluated in two real-world environments, a factory-plant and a regular university building, with results comparable to those obtained by conventional radio map-based solutions.

The accuracy of fingerprinting-based positioning methods accuracy is limited by the fluctuations in the radio signal intensity mainly due to reflections, refractions, and multipath interference, among other factors. We consider that the fluctuations (often modelled as a Gaussian process for simplification purposes) can be minimized by exploiting the richness of multiple signals collected simultaneously through independent network interfaces. This paper introduces an analysis of Wi-Fi signals' statistics using simultaneous measurements which shows that RSSI values obtained from independent devices are not highly correlated. The low correlation between Wi-Fi interfaces might be exploited to improve the positioning accuracy. The validation of the proposed fingerprinting approach in a real scenario shows that the mean and maximum error in positioning can be reduced by more than 40% when five Wi-Fi interfaces are simultaneously used for fingerprinting.