RNN-Based GNSS Positioning using Satellite Measurement Features and Pseudorange Residuals

In the Global Navigation Satellite System (GNSS) context, the growing number of available satellites has lead to many challenges when it comes to choosing the most accurate pseudorange contributions, given the strong impact of biased measurements on positioning accuracy, particularly in single-epoch scenarios. This work leverages the potential of machine learning in predicting link-wise measurement quality factors and, hence, optimize measurement weighting. For this purpose, we use a customized matrix composed of heterogeneous features such as conditional pseudorange residuals and per-link satellite metrics (e.g., carrier-to-noise power density ratio and its empirical statistics, satellite elevation, carrier phase lock time). This matrix is then fed as an input to a recurrent neural network (RNN) (i.e., a long-short term memory (LSTM) network). Our experimental results on real data, obtained from extensive field measurements, demonstrate the high potential of our proposed solution being able to outperform traditional measurements weighting and selection strategies from state-of-the-art

Deep Learning with Partially Labeled Data for Radio Map Reconstruction

In this paper, we address the problem of Received Signal Strength map reconstruction based on location-dependent radio measurements and utilizing side knowledge about the local region; for example, city plan, terrain height, gateway position. Depending on the quantity of such prior side information, we employ Neural Architecture Search to find an optimized Neural Network model with the best architecture for each of the supposed settings. We demonstrate that using additional side information enhances the final accuracy of the Received Signal Strength map reconstruction on three datasets that correspond to three major cities, particularly in sub-areas near the gateways where larger variations of the average received signal power are typically observed.Comment: 42 pages, 39 figure

A Novel Satellite Selection Algorithm Using LSTM Neural Networks For Single-epoch Localization

This work presents a new approach for detection and exclusion (or de-weighting) of pseudo-range measurements from the Global Navigation Satellite System (GNSS) in order to improve the accuracy of single-epoch positioning, which is an essential prerequisite for maintaining good navigation performance in challenging operating contexts (e.g., under Non-Line of Sight and/or multipath propagation). Beyond the usual preliminary hard decision stage, which can mainly reject obvious outliers, our approach exploits machine learning to optimize the relative contributions from all available satellites feeding the positioning solver. For this, we construct a customized matrix of pseudorange residuals that is used as an input to the proposed longshort term memory neural network (LSTM NN) architecture. The latter is trained to predict several quality indicators that roughly approximate the standard deviations of pseudo-range errors, which are further integrated in the calculation of weights. Our numerical evaluations on both synthetic and real data show that the proposed solution is able to outperform conventional weighting and signal selection strategies from the state-of-theart, while fairly approaching optimal positioning accuracy.Comment: arXiv admin note: text overlap with arXiv:2306.0531

Prevision of 3-D trajectories in real time

Pas de résuméPas de résum

Prevision of 3-D trajectories in real time

Pas de résuméPas de résum

UWB-aided GNSS/INS fusion for resilient positioning in GNSS challenged environments

International audienceThe fusion of Inertial Navigation System (INS) and Global Navigation Satellite System (GNSS) is a well established technique to provide resilient positioning even in GNSS chal-lenged environments. However, when GNSS reception conditions are persistently degraded, the inherent drift of inertial navigation can result in insufficient accuracy (e.g. greater than 1m), especially when using industrial or tactical grade Inertial Measurement Unit (IMU). This study introduces the tightly coupled integration of Ultra-Wideband (UWB) ranging measurements with fixed beacons to a loosely coupled GNSS/INS fusion. The algorithm uses an Error-State Kalman Filter (ES-KF) that supports Velocity Constraints (VC) and Zero-Angular Rate Updates (ZARU)/Zero Velocity Updates (ZVU). It details the necessary pre-processing of UWB measurements to correct for clock drift, velocity and latency errors, and provides two calibration techniques suitable for guided and generic use cases, resulting in ranging accuracy of better than 3cm and 11cm, respectively, based on 28 field tests. The benefits of UWB measurements in fusion are demonstrated through a field trial with severely degraded GNSS conditions, resulting in horizontal accuracy better than 40cm (compared to 2.1m without UWB) and improved rejection of poor GNSS measurements

Prévision de trajectoires 3-D en temps réel

STRASBOURG-Sc. et Techniques (674822102) / SudocSudocFranceF

Post-processing optimization of piecewise indoor trajectories based on IMU and RSS measurements

International audiencePost-processing indoor navigation is interesting, for example to develop crowdsourcing analysis. The post-processing framework allows to provide a better estimation than in a real-time framework. The main contribution of this paper is to present a piecewise parametrization using inertial measurement unit (IMU) and received signal strength (RSS) measurements only which lead to an optimization problem. A Levenberg-Marquardt algorithm improved with simulated annealing and an adjustment of RSS measurements data leads to a good estimation (55% of the error less than 5 meters) of the trajectory

Comparison of post-processing algorithms for indoor navigation trajectories

ISBN: 978-1-5090-2425-4nternational Conference on Indoor Positioning and Indoor NavigationInternational audienc