Toward Accurate Indoor Positioning: An RSS-Based Fusion of UWB and Machine-Learning-Enhanced WiFi.

A wide variety of sensors and devices are used in indoor positioning scenarios to improve localization accuracy and overcome harsh radio propagation conditions. The availability of these individual sensors suggests the idea of sensor fusion to achieve a more accurate solution. This work aims to address, with the goal of improving localization accuracy, the fusion of two conventional candidates for indoor positioning scenarios: Ultra Wide Band (UWB) and Wireless Fidelity (WiFi). The proposed method consists of a Machine Learning (ML)-based enhancement of WiFi measurements, environment observation, and sensor fusion. In particular, the proposed algorithm takes advantage of Received Signal Strength (RSS) values to fuse range measurements utilizing a Gaussian Process (GP). The range values are calculated using the WiFi Round Trip Time (RTT) and UWB Two Way Ranging (TWR) methods. To evaluate the performance of the proposed method, trilateration is used for positioning. Furthermore, empirical range measurements are obtained to investigate and validate the proposed approach. The results prove that UWB and WiFi, working together, can compensate for each other's limitations and, consequently, provide a more accurate position solution.

Benefits and Limitations of the Record and Replay Approach for GNSS Receiver Performance Assessment in Harsh Scenarios.

Global navigation satellite systems play a significant role in the development of intelligent transport systems, where the estimation of the vehicle’s position is a key element. However, in strongly constrained environments such as city centers, the definition of quality metrics and the assessment of positioning performances are challenges to be addressed. Due to the variability of different urban scenarios, the modeling of the dynamics as well as the architecture of the positioning platform, which might embed other sensors and aiding means to the GNSS unit, make it hard to define unambiguous positioning metrics. Performance assessment through analytical models and simulators can be ineffective in terms of cost, complexity, and general validity and scalability of the results. This paper shows how a record and replay approach can be an efficient solution to grant fidelity to a realistic scenario. This work discusses advantages and disadvantages with emphasis on the case study of harsh scenarios. Such an approach requires proper data collections that allow the replay phase to test the GNSS-based positioning terminals. This paper presents the results obtained on a set of field tests related to different scenarios, selected as representative for the key performance indicators assessment.

Error Modelling for Multi-Sensor Measurements in Infrastructure-Free Indoor Navigation.

The long-term objective of our research is to develop a method for infrastructure-free simultaneous localization and mapping (SLAM) and context recognition for tactical situational awareness. Localization will be realized by propagating motion measurements obtained using a monocular camera, a foot-mounted Inertial Measurement Unit (IMU), sonar, and a barometer. Due to the size and weight requirements set by tactical applications, Micro-Electro-Mechanical (MEMS) sensors will be used. However, MEMS sensors suffer from biases and drift errors that may substantially decrease the position accuracy. Therefore, sophisticated error modelling and implementation of integration algorithms are key for providing a viable result. Algorithms used for multi-sensor fusion have traditionally been different versions of Kalman filters. However, Kalman filters are based on the assumptions that the state propagation and measurement models are linear with additive Gaussian noise. Neither of the assumptions is correct for tactical applications, especially for dismounted soldiers, or rescue personnel. Therefore, error modelling and implementation of advanced fusion algorithms are essential for providing a viable result. Our approach is to use particle filtering (PF), which is a sophisticated option for integrating measurements emerging from pedestrian motion having non-Gaussian error characteristics. This paper discusses the statistical modelling of the measurement errors from inertial sensors and vision based heading and translation measurements to include the correct error probability density functions (pdf) in the particle filter implementation. Then, model fitting is used to verify the pdfs of the measurement errors. Based on the deduced error models of the measurements, particle filtering method is developed to fuse all this information, where the weights of each particle are computed based on the specific models derived. The performance of the developed method is tested via two experiments, one at a university's premises and another in realistic tactical conditions. The results show significant improvement on the horizontal localization when the measurement errors are carefully modelled and their inclusion into the particle filtering implementation correctly realized.

Overcoming the challenges of BeiDou receiver implementation.

Global Navigation Satellite System (GNSS)-based positioning is experiencing rapid changes. The existing GPS and the GLONASS systems are being modernized to better serve the current challenging applications under harsh signal conditions. These modernizations include increasing the number of transmission frequencies and changes to the signal components. In addition, the Chinese BeiDou Navigation Satellite system (BDS) and the European Galileo are currently under development for global operation. Therefore, in view of these new upcoming systems the research and development of GNSS receivers has been experiencing a new upsurge. In this article, the authors discuss the main functionalities of a GNSS receiver in view of BDS. While describing the main functionalities of a software-defined BeiDou receiver, the authors also highlight the similarities and differences between the signal characteristics of the BeiDou B1 open service signal and the legacy GPS L1 C/A signal, as in general they both exhibit similar characteristics. In addition, the authors implement a novel acquisition technique for long coherent integration in the presence of NH code modulation in BeiDou D1 signal. Furthermore, a simple phase-preserved coherent integration based acquisition scheme is implemented for BeiDou GEO satellite acquisition. Apart from the above BeiDou-specific implementations, a novel Carrier-to-Noise-density ratio estimation technique is also implemented in the software receiver, which does not necessarily require bit synchronization prior to estimation. Finally, the authors present a BeiDou-only position fix with the implemented software-defined BeiDou receiver considering all three satellite constellations from BDS. In addition, a true multi-GNSS position fix with GPS and BDS systems is also presented while comparing their performances for a static stand-alone code phase-based positioning.

A novel approach of creating sustainable urban planning solutions that optimise the local air quality and environmental equity in Helsinki, Finland: The CouSCOUS study protocol.

BACKGROUND: Air pollution is one of the major environmental challenges cities worldwide face today. Planning healthy environments for all future populations, whilst considering the ongoing demand for urbanisation and provisions needed to combat climate change, remains a difficult task. OBJECTIVE: To combine artificial intelligence (AI), atmospheric and social sciences to provide urban planning solutions that optimise local air quality by applying novel methods and taking into consideration population structures and traffic flows. METHODS: We will use high-resolution spatial data and linked electronic population cohort for Helsinki Metropolitan Area (Finland) to model (a) population dynamics and urban inequality related to air pollution; (b) detailed aerosol dynamics, aerosol and gas-phase chemistry together with detailed flow characteristics; (c) high-resolution traffic flow addressing dynamical changes at the city environment, such as accidents, construction work and unexpected congestion. Finally, we will fuse the information resulting from these models into an optimal city planning model balancing air quality, comfort, accessibility and travelling efficiency.