1,720,999 research outputs found
Distinguishing Ionospheric Scintillation from Multipath in GNSS Signals Using Bagged Decision Trees Algorithm
Full text linkThis paper presents a machine learning model able to distinguish between ionospheric scintillation and multipath in GNSS-based scintillation monitoring data. The inputs to the model are the average signal intensity, the variance in the signal intensity, and the covariance between the in-phase and the quadrature-phase outputs of the tracking loop of a GNSS receiver. The model labels the data as either scintillated, multipath affected, or clean GNSS signal. The overall accuracy of the model is 96% with 2% miss-detection rate and a negligible false alarm rate for the scintillation class in particular. The gain in the amount of scintillation data is up to 17.5% that would have been discarded if an elevation mask of 30° was implemented
DGNSS Cooperative Positioning in Mobile Smart Devices: A Proof of Concept
Full text linkGlobal Navigation Satellite System (GNSS) constitutes the foremost provider for geo-localization in a growing number of consumer-grade applications and services supporting urban mobility. Therefore, low-cost and ultra-low-cost, embedded GNSS receivers have become ubiquitous in mobile devices such as smartphones and consumer electronics to a large extent. However, limited sky visibility and multipath scattering induced in urban areas hinder positioning and navigation capabilities, thus threatening the quality of position estimates. This work leverages the availability of raw GNSS measurements in ultralow-cost smartphone chipsets and the ubiquitous connectivity provided by modern, low-latency network infrastructures to enable a Cooperative Positioning (CP) framework. A Proof Of Concept is presented that aims at demonstrating the feasibility of a GNSS-only CP among networked smartphones embedding ultra-low-cost GNSS receivers. The test campaign presented in this study assessed the feasibility of a client-server approach over 4G/LTE network connectivity. Results demonstrated an overall service availability above 80%, and an average accuracy improvement over the 40% w.r.t. to the GNSS standalone solution
A Comparative Performance Analysis of GPS L1 C/A, L5 Acquisition and Tracking Stages under Polar and Equatorial Scintillations
Full text linkThis paper provides a comparative performance analysis of different acquisition and tracking methods of GPS L1 C/A and GPS L5 signals testing their robustness to the presence of scintillations in the propagation environment. The paper compares the different acquisition methods in terms of probabilities of detection/false alarm, peak-to-noise floor ratios for the acquired signal and execution time, assessing the performance loss in the presence of scintillations. Moreover, robust tracking architectures that are optimized to operate in a harsh ionospheric environment have been employed. The performance of the carrier tracking methods, namely, traditional Phase-Locked Loop (PLL) and Kalman filter based-PLL, have been compared in terms of the standard deviation of Doppler estimation, phase error, phase lock indicator (PLI) and phase jitter. The study is based on real GNSS signals affected by significant phase and amplitude scintillation effects, collected at the South African Antarctic research base (SANAE IV) and Brazilian Centro de Radioastronomia e Astrofisica Mackenzie (CRAAM) monitoring stations. Performance is assessed exploiting a fully software GNSS receiver which implements the different architectures. The comparative analysis allows to choose the best setting of the acquisition and tracking parameters, in order to allow the operation of signal acquisition and tracking at a required performance level under scintillation conditions
Analysis of multi-constellation GNSS PPP solutions under phase scintillations at high latitudes
Full text linkIn the past few years, the rapid evolution of multi-constellation navigation satellite systems boosted the development of many scientific and engineering applications. More than 100 satellites will be available in a few years, when all the four global constellations (GPS, GLONASS, Galileo, and Beidou) will be fully deployed. This high number of visible satellites has improved the performance of precise point positioning (PPP) techniques both in terms of accuracy and of session length, especially easing the modeling of ionospheric biases. However, in the presence of severe environmental and atmospheric conditions, the performance of PPP considerably deteriorates. It is the case of high-latitude scenarios, where the satellites coverage is limited, the satellites geometry is poor and ionospheric scintillation are frequent. This paper analyzes the quality of PPP solutions in terms of accuracy and convergence time, for a GNSS station in Antarctica. Single and multi-constellation results are compared, proving the benefits of the availability of a higher number of satellites as well as the improved robustness to the presence of moderate and strong phase scintillations. The use of PPP multi-constellation at high latitudes is indeed essential to guarantee high accuracy, and to obtain a low convergence time, of the order of tens of minutes
Multipath detection based on K-means clustering
No full textThe aim of this paper is to propose a multipath detection algorithm, which is based on K-means clustering that belongs to the class of unsupervised machine learning algorithms. The algorithm processes measurement sets computed for each satellite, namely, carrier phase, pseudorange and carrier-to-noise ratio, creating clusters of consistent measurements, thus allowing the identification of satellite signals suffering from the multipath error. Since it is an unsupervised method, it overcomes one of the most limiting features of supervised algorithms that require training data sets a-priori obtained as representative of multipath and no-multipath conditions. The study exploits both the real GNSS data affected by the multipath in the surrounding environment that were collected at South African Antarctic research base SANAE-IV and the simulated data where the ionospheric, tropospheric and multipath errors are modelled. Receiver Autonomous Integrity Monitoring (RAIM) algorithm with parity method was also implemented and tested for the same datasets, and it will be used as a term of comparison for the algorithm performance
An overview on Global Positioning Techniques for Harsh Environments
Full text linkAbstract This chapter presents strategies and techniques used to increase the sensitivity of global navigation satellite system (GNSS) receivers in order to make them usable in harsh environments, such as urban canyons, light indoor scenarios, deep forests, or space. It discusses the assistance that can be provided to the GNSS receiver through communication channels to ease the acquisition and tracking processes. Assisted GNSS is a consolidated standard, but other kinds of assistance and signal processing techniques can improve the ability of the receiver to process the signal at a low signal-to-noise ratio. The chapter introduces the common approaches to increase the sensitivity at the acquisition stage, discussing the impact on the accuracy of the delay and Doppler shift estimation, and the intrinsic limitation to coherent and noncoherent integration time extension. Techniques to increase robustness to low signal-to-noise ratio scenarios are presented, considering the structure of new and modernized GNSS signals
The Growing Window Algorithm: a Sub-Optimal Strategy for Image Irregular Samples Selection
No full textThe new algorithm proposed in this paper is a novel method for sample selection based on a quasi-random search
within an image. As far as the general sampling problem is concerned, a brief introduction on the well-known
sampling algorithm is presented in order to introduce the most important parameters to be taken into account for
the performances evaluation of the novel method here proposed. Then, the Growing Window Irregular Selection
is described following few steps: obviously, the purpose of this irregular sampling strategy is to increase the
sample density in the zones of strong luminance variance of the image. On the contrary, in zones with no
variation almost no sample are allocated. Several tests have been performed on different images (e.g. geophysical
and medical) and interesting results have been outlined
Opportunistic use of GNSS Signals to Characterize the Environment by Means of Machine Learning Based Processing
Full text linkGNSS is widely used to provide positions in an absolute reference frame in Unmanned Aerial Vehicles (UAV) and Unmanned Ground Vehicles (UGV), where GNSS is merged with the information provided by other sensors. Even if the main goal of GNSS signal processing is the positioning, multifrequency signals are a rich source of information about the propagation environment surrounding the mobile vehicle. In urban and harsh environment, situational awareness is essential to tailor the operations and take proper countermeasure to harsh propagation conditions. Given this framework the present paper will describe the use of GNSS as signals of opportunity for the characterization of the operative environment by processing the GNSS observables through Machine Learning (ML) algorithms that can be used as efficient features extractors. The paper will present some case studies of operational scenarios for UGVs and for a static monitoring station, showing how through combining DSP techniques with both unsupervised and supervised ML algorithms (K-means classes, Support Vector Machines) it is possible to retrieve the information about the propagation scenario for multipath, interference and atmospheric limitations
UAV-based GNSS-R for water detection as a support to flood monitoring operations: A feasibility study
Full text linkSignals from global navigation satellite systems (GNSS) can be utilized as signals of opportunity in remote sensing applications. Geophysical properties of the earth surface can be detected and monitored by processing the back-scattered GNSS signals from the ground. In the literature, several airborne GNSS-based passive radar experiments have been successfully demonstrated. With the advancements in small unmanned aerial vehicles (UAVs) and their applications for environmental monitoring, we want to investigate whether GNSS-based passive radar can provide valuable geospatial information from such platforms. Low-cost GNSS reflectometry sensors, developed using commercial of the shelf components, can be mounted onboard UAVs and flown to sense environmental parameters. This paper presents the results of a preliminary study to investigate the feasibility of utilizing data collected by UAV-based GNSS-R sensors to detect surface water for a potential application in supporting flood monitoring operations. The study was conducted in the area surrounding the Avigliana lakes in Northern Italy. The results show the possibility of detecting small water surfaces with few tens of meters resolution, and estimating the area of the lake surface with 92% accuracy. Furthermore, it is proved through simulations that the use of multi-GNSS increases this accuracy to about 99%
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