U-blox (Switzerland)

<title>Abstract</title> Global navigation satellite system (GNSS) positioning relies on accurate stochastic models for measurement errors to provide reliable position estimates and bounds. This paper presents a novel approach for modeling errors in GNSS measurements with Bayesian distributional regression, using variational inference to scale model complexity and data set size beyond typical computational limits. This methodology is applied to an automotive data set for GNSS pseudorange (multipath) errors, targeting a Student’s t-distributed model to realistically characterize heavy-tailed data. The distribution is regressed on signal quality indicators from the GNSS receiver to segment different environmental effects on the measurements. Bayesian penalized tensor product splines are used to model nonlinear relationships based on the signal quality indicators. Detailed analyses of goodness-of-fit diagnostics show that the model is able to fit well to the data, and model uncertainty is quantified such that users may be aware of and compensate for inaccuracies in modeling.

Zoonotic malaria risk at human-wildlife-environment interfaces requires surveillance that integrates signals from reservoirs, vectors and the environment. We coupled a drone-based environmental DNA (eDNA) canopy swabbing approach with portable quantitative PCR (qPCR) to detect Plasmodium DNA in situ during a 24-h field exercise in the Amazon rainforest. Drone-lowered sterile swabs into the canopy, which were then extracted and subjected to a multiplex pan-Plasmodium assay targeting five human-infecting Plasmodium species (limit of detection 0.2 parasites μL−1). Of 12 samples (10 canopy swabs, 2 field blanks; 13 total runs including repeats), one canopy swab amplified in duplicate (Ct = 28.7 and 29.23), while positive controls amplified as expected (Ct = 30.82 and 31.11) and all other environmental samples and blanks were negative. Passive acoustics confirmed co-occurring howler monkeys (Alouatta spp.), a known reservoir, whereas Anopheles mosquitoes were not recovered from concurrently deployed insect canopy traps. The end-to-end workflow, from drone deployment to qPCR diagnostic readout, averaged 1.5 h per assay, without requiring cold-chain logistics. This proof-of-concept demonstrates that intracellular parasite DNA can be recovered from canopy surfaces and read out in real-time, providing upstream, landscape-level intelligence to guide targeted vector surveillance in remote settings. Our approach operationalizes One Health by integrating environmental, wildlife, and vector signals within a single technological platform, representing a paradigm shift from reactive, sector-specific surveillance to proactive, integrated pathogen intelligence across the human-animal-environment interface.

The emergence of 6G networks paves the way for a new era of Artificial Intelligence (AI)-empowered integration of Internet of Things (IoT), autonomous mobile robots (AMRs) and extended reality (XR). This will revolutionise the ways users (e.g., humans, robots) interact with each other and their surroundings expanding their awareness beyond immediate boundaries. Driven by demand for more effective emergency response fuelled by adverse and increasing impacts of climate change, in this paper, we conceptually explore an architectural framework for intelligent emergency response services in 6G networks. The proposed architecture bridges an evolution of supporting technologies in 5G with future intelligent emergency response service requirements. We identify integration of 3D networking (3DN) and ambient IoT (A-IoT), empowered by native AI, network sensing and localisation, as key technologies for the proposed framework. We conclude the paper with discussion on current technology gaps and recommendations that would lead towards future 6G intelligent emergency response.

In this paper, we present a novel Deep Neural Network-based indoor localization method that estimates the position of a Bluetooth Low Energy (BLE) transmitter (tag) by using the received signals’ characteristics at multiple Anchor Points (APs). We use the received signal strength indicator (RSSI) value and the in-phase and quadrature-phase (IQ) components of the received BLE signals at a single time instance to simultaneously estimate the angle of arrival (AoA) at all APs. Through supervised learning on simulated data, various machine learning (ML) architectures are trained to perform AoA estimation using varying subsets of anchor points. In the final stage of the system, the estimated AoA values are fed to a positioning engine which uses the least squares (LS) algorithm to estimate the position of the tag. The proposed architectures are trained and rigorously tested on several simulated room scenarios and are shown to achieve a localization accuracy of 70 cm. Moreover, the proposed systems possess generalization capabilities by being robust to modifications in the room’s content or anchors’ configuration. Additionally, some of the proposed architectures have the ability to distribute the computational load over the APs.

Evil Waveforms (EWF) are non-nominal distortions that can be observed on satellite signals and cause additional bias on the estimated user position. A Threat Model (TM) has been proposed by ICAO for GPS L1 C/A to describe the possible distortions that can be observed on the GPS signals [1]. This Threat model is also adopted for Galileo E1-C and E5a-Q signals. A previous paper focused on the Signal Quality Monitoring (SQM) design and compliance test for Galileo signals. In the present paper, the effect of the pre-correlation filter design applied at the aircraft and reference station receivers on the EWF induced bias and SQM compliance test is assessed. Two aspects of the filter design are considered: the filter center frequency shift due to temperature variation or electronic component aging, and the filter gain roll-off out of the filter bandwidth. The obtained results of EWF differential biases and SQM test in [2] are considered as the baseline results used to evaluate the effect on the tested filter parameters. This paper concludes on the tolerable center frequency offset and gain roll-off based on the comparison of the obtained EWF results for Galileo E1 and E5a to the baseline one.

This letter formally proves the statistical inconsistency of the Allan variance-based estimation of latent (composite) model parameters. This issue has not been sufficiently investigated and highlighted since it is a technique that is still being widely used in practice, especially within the engineering domain. Indeed, among others, this method is frequently used for inertial sensor calibration, which often deals with latent time series models and practitioners in these domains are often unaware of its limitations. To prove the inconsistency of this method, we first provide a formal definition and subsequently deliver its theoretical properties, highlighting its limitations by comparing it with another statistically sound method.

This paper aims at studying the behaviour of the errors coming from inertial sensors when measured in dynamic conditions. After proposing a method for constructing the error process, the properties of these errors are estimated via the Generalized Method of Wavelets Moments methodology. The developed model parameters are compared to those obtained under static conditions. Finally an attempted is presented to find the link between the encountered dynamic of the vehicle and error-model parameters.