1,721,024 research outputs found
GNSS-only Collaborative Positioning Methods for Networked Receivers
Full text linkL'abstract è presente nell'allegato / the abstract is in the attachmen
Black-Boxing GNSS Signals Post-Processing Through Machine Learning for Multi-Agent Collaborative Positioning of IoT Devices
Full text linkNowadays, GNSS (GNSS) receivers are embedded in a variety of electronics devices, and a growing number of users rely on them to track their position, velocity, and time. The density of Global Navigation Satellite System (GNSS) receiver has especially increased in urban areas with the advent of small-scaled IoT devices. Due to the limited GNSS signal power and the variability of the environment, continuous GNSS signal tracking may represent a demanding task for receivers, which, in addition, have to perform demodulation of the navigation message to provide the user with meaningful information. Furthermore, when operated by low-power platforms, such as Internet of Things (IoT) devices, the aforementioned tasks may quickly drain the battery. On the other hand, the low-power-consumption network connectivity hosted by IoT electronics could represent an ideal environment to enable new patterns for their state estimation, based on collaborative, multi-agent Position Navigation and Time (PNT) methods that would not imply continuous operation of the embedded GNSS receiver. This preliminary study aims to understand whether machine learning techniques could support such paradigms for position estimation in IoT devices. We generated an artificial environment, where conventional GNSS share their multi-satellite delay-Doppler matrices and their positions. These data are meant to be used to estimate a “reference” IoT receiver position. We used two open-source libraries, XGBoost, to implement a gradient-boosted decision tree, and Keras, to implement a multi-layer perceptron. The estimation error on the IoT receiver position obtained using machine learning tools is lower (typically, 10 % to 20 %) than the estimation error returned by a simplistic reference model, based on the arithmetic average of the networked receivers’ positions. The results suggest that rudimentary ML algorithms can extract fundamental information from collaborative users, thus opening new frontiers to collaborative GNSS navigation
Gaborized MCS for Precise Code Phase Offset Estimation in Radionavigation
Full text linkIn radar and radionavigation systems, waveform design optimization has mostly focused on Gabor bandwidth (GB), as a metric to infer code phase offset estimation accuracy. While optimization algorithms have maximized GB w.r.t. to specific design parameters and receiver bandwidth, they lack closedform solutions for pulse shapes. Inspired by Gabor pulse theory, this paper presents a family of waveforms shaping multi-level coded spreading (MCS) coefficients with a Gaussian envelope, namely the Gaborized-MCS (G-MCS). These waveforms achieve nearly optimal GB performance, reflecting the trend of current optimization techniques to agnostically converge to such pulse shapes. Numerical analyses characterize the GB of the proposed signals across varying receiver bandwidths and compare them to optimized waveforms and legacy binary offset carrier modulations largely adopted in Global Navigation Satellite Systems
On the Use of Meta-Signals to Counteract Ionospheric Phase Advance Effects on Wideband LEO PNT Signals
Full text linkThe recent, massive deployment of Low-Earth Orbit (LEO) satellites constellations for broadband communication services is attracting the attention of the Positioning, Navigation and Timing (PNT) community concerning the possibility of complementing current Global Navigation Satellite System (GNSS) through LEO radionavigation signals. Motivated by lower free-space path loss brought in by smaller orbital radia, legacy radionavigation frequency bands as well as less congested bands such as S, C, X, Ka and Ku could host dedicated radionavigation signals or combined solutions that hybridize them with communications signals. However, as per current GNSS signals, LEO radionavigation counterparts will still suffer the effects of intense ionospheric activity. In particular, in spite of higher carrier frequencies which limit the impact of their interaction with the ionosphere, non-negligible ionospheric-induced phase advance has the potential of impoverishing the correlation performance of wideband modulation schemes that may be intrinsically of interest in the design of wide and ultra-wideband navigation signals from LEO. Building on insights from previous literature on Galileo wideband signals and their interactions with ionosphere, we analyze the dispersive effect of the ionosphere on a sample wideband channel of 128 MHz hosting signals with different modulations. We then propose the use of metasignals to overcome the limitation of native wideband signals such as high-order Binary Offset Carrier modulations. The results demonstrate that simplistic BPSK-based meta-signals can be designed to be more robust than wideband BPSK modulations and bandwidth-equivalent BOC signals
A Navigation Signals Monitoring, Analysis and Recording Tool: Application to Real-Time Interference Detection and Classification
Full text linkGiven the extensive dependency on Global Navigation Satellite Systems (GNSS) for several crucial applications, the disruption caused by intentional or unintentional Radio Frequency Interference (RFI) may dramatically affect reliability and poses potential threats to various operations dependent on such systems. Recently, these threats have increased, and their detection and mitigation are of utmost importance in the field. To this aim, this paper presents an architecture for real-time detection and classification of RFI affecting multi-band GNSS signals based on a machine learning method. This study proposes an architecture combining an actual GNSS monitoring station for recording GNSS signals (Navigation Signals Monitoring, Analysis, and Recording Tool (N-SMART) system) with a deep neural network approach to detect and classify different classes of interferences. The proposed approach enables continuous monitoring, recording, and prompt alerting of RFI occurrences in multi-band GNSS signals, by leveraging the flexibility of a Software Defined Radio and docker frameworks. The design and deployment aspects of the proposed architecture are discussed, and the performance of the classification algorithm is evaluated. The results of the experimental test campaign on real interfered GNSS signals showed an overall accuracy of 85% and they highlighed the potential for effective, real-time classification of RFIs in GNSS
Differential GNSS for Ranging and Synchronization Between Lunar Orbiters: Impact of Large Baselines and Relative Dynamics
No full textAs lunar exploration progresses, autonomous space-
craft navigation is becoming critical. Global Navigation Satel-
lite System (GNSS) signals provide a valuable alternative to
traditional ground-based systems, offering real-time navigation
for lunar orbiters. NASA’s LuGRE initiative recently demon-
strated GNSS signals reception at 432,384 km from Earth,
proving its feasibility despite challenges such as low power
levels and poor geometry. These limitations justify the need
for augmentation strategies to extend GNSS usability in the
Space Service Volume (SSV) and potentially support future lunar
navigation systems. This study investigates cooperation between
lunar orbiters via Differential GNSS (DGNSS) where missions are
assumed to exchange pseudorange and Doppler measurements to
estimate their Inter-Spacecraft Ranges (ISR). A key challenge
is the asynchronous nature of pseudorange data caused by
clock biases because of the large dynamics characterizing such
scenario. Therefore, this work provides an assessment of a
time-extrapolation technique used to mitigate these offsets and
then evaluates its impact on the ISR estimation process using
simulated data. The findings provide insights into DGNSS as a
potential augmentation solution, supporting the roadmap towards
a robust lunar navigation architecture
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
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
Surveying GNSS Carrier Offset Modulations: Investigating Gabor Uncertainty Principle for Precise Time Delay and Frequency Offsets Estimation
Full text linkIn the last decades, the adoption of offset carrier modulations represented one of the main aspects in the modernization of Global Navigation Satellite System (GNSS) signals. Offset carrier modulations provide indeed specific signal characteristics and guarantee the desired performance trade-off in terms of bandwidth utilization and tracking jitter at the receiver. In light of this, ongoing signal design proposals for modernized GNSS, Low-Earth Orbit and navigation services cannot neglect fundamental findings in this direction. At the same time, the theoretical bounds governing time delay and frequency offset estimation have a direct impact on receivers state estimation when this task rely on the inference of signal-derived observables. In this context, the aim of this work is to investigate the inherent relationship between offset carrier modulation, i.e., spreading code chip shaping, and the bounds set by uncertainty principle about time delay and frequency offsets estimation of GNSS signals. The research addresses a surveying analysis of currently-adopted offset carrier modulations and the evaluation of their theoretical bounds associated to the respective analytical ambiguity functions. The study offers a methodology to synoptically compare different chip shaping and to characterize how this influences signals' time-frequency localization precision and estimation errors at the receiver, which has a direct impact on delay and frequency lock loops performance at the receiver tracking stage
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
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