1,720,970 research outputs found
Design of Advanced Positioning Solutions: A Bayesian Approach
Full text linkL'abstract è presente nell'allegato / the abstract is in the attachmen
A GNSS Multipath and NLoS Mitigation Method for Urban Scenarios Based on Particle Filtering
Full text linkGlobal navigation satellite systems (GNSSs) are at the basis of many location services. However, in harsh environments such as urban canyons, the performance can be highly degraded due to lack of satellite visibility and complex reflection phenomena like multipath and Non-Line-of-Sight (NLoS). This work aims at exploiting the consistency of the information provided by GNSS receivers to detect and mitigate the effect of multipath and NLoS on the positioning solution. The proposed method extends the definition of innovation for the Particle Filter (PF), while also exploiting its native capability to handle more complex probability models of the errors. The use of multi-modal probability densities adds robustness to the filter in harsh conditions. The proposed method has been tested on real open-source datasets, showing considerable improvement in terms of position error compared to other state-of-the-art solutions based on the Extended Kalman Filter (EKF)
Improved Outdoor Target Tracking via EKF-based GNSS/UWB Tight Integration with Online Time Synchronisation
Full text linkAccurate and robust positioning technology in the mass-market segment is pivotal to support a number of critical Positioning, Navigation and Timing (PNT) applications. State-of-the-art Global Navigation Satellite System (GNSS) receivers design has been increasingly targeting flexible, embedded architectures integrating low-cost sensors to overcome GNSS limitations. The widespread proliferation of Ultra-Wide Band (UWB) technology, which enables centimeter-level accurate ranging in cluttered environments, is an appealing candidate for tight hybridisation with GNSS. When dealing with data streams from different Commercial-Off-The-Shelf (COTS) sensors, it is known that temporal misalignment is of concern, and accurate state-estimation via centralised, recursive filtering architectures can be undermined. As a first contribution, this work theoretically analyses the accuracy impact of asynchronous data association in the framework of a tightly integrated GNSS/UWB system leveraging plain Extended Kalman Filter (EKF) integration. Then, it puts forward a novel EKF-based model implementing online time offset estimation and compensation (i.e., time calibration) for GNSS/UWB tight integration. Results obtained in a multi-agent, cooperative scenario demonstrate that the proposed hybridisation methodology can achieve horizontal and vertical positioning accuracy gains of \SI{33.95}{\%} and \SI{59.33}{\%} , respectively, in Root-Mean-Square Error (RMSE) terms
Comparison of GNSS Multipath/NLoS Characterization Between Geodetic Receivers and Smartphones Across GPS L1 C/A and L5 Signals
Full text linkThe issue of multipath and Non-Line-of-Sight (NLoS) interferences has significantly impacted the performance of Global Navigation Satellite System (GNSS) services in various emerging applications, such as autonomous vehicles and smart wearables. Characterizing the statistical pattern for multipath/NLoS interference might enlighten the development of techniques for detecting and mitigating such interferences. For this purpose, this research first introduces a method to estimate pseudorange biases caused by multipath/NLoS using a clustering algorithm. Then, the estimation method for the multipath/NLoS bias is extended to dual-frequency GNSS signals, including Global Positioning System (GPS) L1 C/A and L5. Subsequently, an experiment is carried out to collect and analyze real-world static GNSS data under multipath/NLoS environments. This analysis involved a comparative study of multipath/NLoS patterns using both a geodetic receiver and a smartphone across GPS L1 C/A and L5 signals. The experimental results uncovered some patterns in multipath/NLoS behaviors, offering insights that could potentially guide the development of new algorithms to detect and mitigate such interferences
Pseudorange and Doppler-Based Positioning: Enabling Convergence of Least-Squares Estimation from MEO to LEO
Full text linkThe growing interest of the space industry in satellite systems within the low Earth orbit (LEO) region has prompted attention to their potential for positioning, navigation, and timing applications. This study addresses the convergence issue highlighted in the literature when the Gauss Newton (GN) method is applied to least squares (LS) position estimation algorithms in LEO scenarios, with analyses conducted independently for pseudorange and Doppler shift measurements. To address these limitations, this paper examines two line search techniques in combination with the GN method. A comprehensive analysis of the LS method is conducted through tests on satellite constellations at various orbital altitudes, from medium Earth orbits to LEOs. The results, evaluated in terms of the number of iterations required to achieve convergence, show that adjusting the GN step using a damping factor, namely the damped GN factor, effectively resolves convergence issues, even in LEO scenarios. In particular, the proposed algorithm consistently converges in the LEO region within an average of seven iterations
A Comparison Study Between the EKF and SIR-PF for GNSS/UWB Tight Integration
Full text linkThe tight integration of Global Navigation Satellite Systems (GNSSs) and low-cost Ultra-Wide Band (UWB) is a prospective positioning solution for autonomous mobile robots that operate in harsh environments with poor satellite visibility. Thanks to the complementarity of the two systems in terms of coverage and ranging performance, the UWB nodes can be used as anchors providing additional ranging measurements. However, the selection of the integration scheme may be a critical issue since high-accuracy positioning performance has to be traded off with the computational complexity of the implementation. This paper compares the performance of two common Bayesian filtering algorithms - the Extended Kalman Filter (EKF) and the Sequential Importance Resampling Particle Filter (SIR-PF) - for the GNSS/UWB tight integration in a dynamic environment. Considering the error sources triggered by the linear approximation employed in the EKF, simulation results show that the performance of the EKF deteriorates more than the SIR-PF when the user's kinematics changes rapidly and when the user gets close to the UWB anchor. Compared to the EKF, the SIR-PF can therefore guarantee superior positioning accuracy even if at the cost of higher computational complexity
GNSS Precise Point Positioning in Cislunar Space: A Study on Regularized Least Squares and Availability
Full text linkThe ongoing Lunar GNSS Receiver Experiment (LuGRE) mission is demonstrating that Global Navigation Satellite System (GNSS) can be a major enabler for radionavigation in cislunar space and on the Moon, offering a complementary solution to ground-based tracking infrastructures. However, cislunar Orbit Determination (OD) and timing with GNSS signals remains challenging due to severe pathloss effects, frequent side lobe receptions, and degraded satellite geometry. This study evaluates a single-frequency precise point positioning (SF-PPP) approach for kinematic OD, leveraging the group and phase ionospheric calibration (GRAPHIC) model to process undifferenced code and phase observations. The method incorporates Tikhonov regularization within a batch nonlinear least square (LS) estimator to tackle the ill-conditioning caused by the inherent rank deficiency of the positioning model. The algorithm is assessed through post-processing of raw GNSS observables collected during a hardware-in-the-loop (HIL) test, simulating representative LuGRE payload operations. Results show that the proposed regularized estimator ensures more than 89 % solution availability in most of the scenarios and achieves sub-kilometer positioning accuracy, even in scenarios with insufficient measurement redundancy
Improved weighting in particle filters applied to precise state estimation in GNSS
Full text linkIn the last decades, the increasing complexity of the fusion of proprioceptive and exteroceptive sensors with Global Navigation Satellite System (GNSS) has motivated the exploration of Artificial Intelligence related strategies for the implementation of the navigation filters. In order to meet the strict requirements of accuracy and precision for Intelligent Transportation Systems (ITS) and Robotics, Bayesian inference algorithms are at the basis of current Positioning, Navigation, and Timing (PNT). Some scientific and technical contributions resort to Sequential Importance Resampling (SIR) Particle Filters (PF) to overcome the theoretical weaknesses of the more popular and efficient Kalman Filters (KFs) when the application relies on non-linear measurements models and non-Gaussian measurements errors. However, due to its higher computational burden, SIR PF is generally discarded. This paper presents a methodology named Multiple Weighting (MW) that reduces the computational burden of PF by considering the mutual information provided by the input measurements about the unknown state. An assessment of the proposed scheme is shown through an application to standalone GNSS estimation as a baseline of more complex multi-sensors, integrated solutions. By relying on the a-priori knowledge of the relationship between states and measurements, a change in the conventional PF routine allows performing a more efficient sampling of the posterior distribution. Results show that the proposed strategy can achieve any desired accuracy with a considerable reduction in the number of particles. Given a fixed and reasonable available computational effort, the proposed scheme allows for an accuracy improvement of the state estimate in the range of 20–40%
A Post-Processing Multipath/NLoS Bias Estimation Method Based on DBSCAN
Full text linkPositioning based on Global Navigation Satellite Systems (GNSSs) in urban environments always suffers from multipath and Non-Line-of-Sight (NLoS) effects. In such conditions, the GNSS pseudorange measurements can be affected by biases disrupting the GNSS-based applications. Many efforts have been devoted to detecting and mitigating the effects of multipath/NLoS, but the identification and classification of such events are still challenging. This research proposes a method for the post-processing estimation of pseudorange biases resulting from multipath/NLoS effects. Providing estimated pseudorange biases due to multipath/NLoS effects serves two main purposes. Firstly, machine learning-based techniques can leverage accurately estimated pseudorange biases as training data to detect and mitigate multipath/NLoS effects. Secondly, these accurately estimated pseudorange biases can serve as a benchmark for evaluating the effectiveness of the methods proposed to detect multipath/NLoS effects. The estimation is achieved by extracting the multipath/NLoS biases from pseudoranges using a clustering algorithm named Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The performance is demonstrated using two real-world data collections in multipath/NLoS scenarios for both static and dynamic conditions. Since there is no ground truth for the pseudorange biases due to the multipath/NLoS scenarios, the proposed method is validated based on the positioning performance. Positioning solutions are computed by subtracting the estimated biases from the raw pseudoranges and comparing them to the ground truth
LuNART-q: The LuGRE Quick Navigation Analysis and Reporting Tool
Full text linkThe Lunar GNSS Receiver Experiment (LuGRE) quick Navigation Analysis and Reporting Tool (LuNART-q) is a MATLAB-based platform developed by Politecnico di Torino for the Italian Space Agency (ASI) and in collaboration with NASA's SCAN program and Qascom S.r.l. Derived from a broader scientific framework for the LuGRE mission, it enables rapid visualization, validation and exploitation of GNSS data acquired in lunar and cislunar environments. LuNART-q automates telemetry parsing, analysis and post-processing of GNSS observables and I/Q batch analysis, navigation performance evaluation, and structured reporting. Twelve configurable "quick experiments", derived from LuGRE Science Definition Team Report objectives, produce experiment reports that can be consolidated into operation summary reports for immediate assessment. Visualization modules allow prompt analysis of signal availability, occultations, and receiver performance, while compliance with Ground System Working Group standards ensures interoperability. An open project based on LuNART-q will be released to allow the community to immediately explore LuGRE open data from the mission, fostering reproducibility, education, and collaborative research. By integrating predictive analysis, adaptive experiment execution, and reproducible reporting, LuNART-q provides an innovative framework that maximized the scientific return of the LuGRE mission and paves the way for companion software designed to support and advance scientific objectives in future missions
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