A Comprehensive Review of the GNSS with IoT Applications and Their Use Cases with Special Emphasis on Machine Learning and Deep Learning Models

This paper presents a comprehensive review of the Global Navigation Satellite System (GNSS) with Internet of Things (IoT) applications and their use cases with special emphasis on Machine learning (ML) and Deep Learning (DL) models. Various factors like the availability of a huge amount of GNSS data due to the increasing number of interconnected devices having low-cost data storage and low-power processing technologies - which is majorly due to the evolution of IoT - have accelerated the use of machine learning and deep learning based algorithms in the GNSS community. IoT and GNSS technology can track almost any item possible. Smart cities are being developed with the use of GNSS and IoT. This survey paper primarily reviews several machine learning and deep learning algorithms and solutions applied to various GNSS use cases that are especially helpful in providing accurate and seamless navigation solutions in urban areas. Multipath, signal outages with less satellite visibility, and lost communication links are major challenges that hinder the navigation process in crowded areas like cities and dense forests. The advantages and disadvantages of using machine learning techniques are also highlighted along with their potential applications with GNSS and IoT

Robust Design of Machine Learning based GNSS NLOS Detector with Multi-Frequency Features

The robust detection of non-line-of-sight (NLOS) signals is of vital importance for land-based and close-to-land safe navigation applications. Their reception and use without adapted mitigation may induce unacceptable inaccuracy and loss of safety. Due to the complex signal conditions in urban environments, the use of machine learning or artificial intelligence techniques and algorithms have recently shown as potential tools to classify GNSS LOS/NLOS signals. The design of machine learning algorithms with GNSS features is an emerging approach that must however, be tackled carefully to avoid biased estimation results and guarantee generalized algorithms for different scenarios, receivers, antennas and their specific installations and configurations. This work has provided new options to guarantee a proper generalization of trained algorithms by means of a pre-normalization of features with models extracted in open-sky (nominal) scenarios. The second main contribution focused on designing a branched (or parallel) machine learning process to handle the intermittent presence of GNSS features in certain frequencies. This allows to exploit measurements in all available frequencies as compared to current approaches in the literature based only on single frequency features. The detection by means of logistic regression not only provides a binary LOS/NLOS decision, but also an associated probability which can be used in the future as a mean to weight specific measurements. The detection with the proposed branched logistic regression with pre-normalized multi-frequency features has shown better results than the state of the art, reaching more than 90% detection accuracy in the validation scenarios evaluated

Effects of Site-Dependent Errors on the Accuracy of C/A Code DGPS Positioning

Several differential GPS processing techniques can be used; for instance, single differencing and double differencing, which are popular in practice. Irrespective of the DGPS processing technique used, the ultimate accuracy of the user-location depends on the existence of non-common or site-dependent errors, which occur at the points of observation and the reference. Of these, the most common and dominant site-dependent error is the multipath. Therefore, this research evaluates the effects of site-dependent errors on C/A code differential GPS correction accuracies by providing special emphasis on the multipath error. For the analyses, four segments of about 24-hour continuous static C/A code based DGPS observations were conducted at three precisely known ground stations and four different multipath environments were introduced by placing three different types of artificial signal reflectors at one of the observation stations. By using the known GPS receiver-reflector configuration, pseudo-range multipath was precisely calculated for each observation segment. C/A code DGPS positioning accuracies before and after multipath mitigation were presented by evaluating the effect of the most dominant site-dependent error, i.e., multipath, on C/A code DGPS correction accuracies

Reason-Resilience and security of geospatial data for critical infrastructures

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Multipath detection from GNSS observables using gated recurrent unit

One of the most used Position, Navigation, and Timing (PNT) technology of the 21st century is Global Navigation Satellite Systems (GNSS). GNSS signals are affected by urban canyons that limit Line-Of-Sight (LOS) and increase position ambiguity. Smart cities are expected to adopt autonomous Unmanned Aerial Vehicles (UAV) operations for critical missions such as the transportation of organs that are time-sensitive. Therefore, techniques to mitigate Non-Line-Of-Sight (NLOS) interference are required for improved positioning accuracy. This paper proposes a Gated Recurrent Unit-based (GRU) multipath detection algorithm that uses pseudorange, ephemerides, Doppler shift, Carrier-To-Noise Ratio (C/N0), and elevation data from each satellite to determine whether multipath is present. Signals from the satellite classified as multipath are then flagged and ignored for Position, Velocity, and Timing (PVT) calculations until they are deemed as LOS. The classification algorithm is developed and tested on Spirent GSS7000 to generate GNSS Radio Frequency (RF). OKTAL-SE Sim3D is used to simulate urban canyon environments in which signals propagate from the satellite to the receiver. RF signals are then transmitted to a Ublox F9P GNSS receiver that can receive GPS and GLONASS signals which are processed to output PVT information. The data collected is used to train the GRU to classify received signals as no multipath or multipath. From performance evaluation, GRU outperforms decision tree, K-Nearest Neighbor (KNN) classifiers, and Support Vector Machines (SVM). Furthermore, comparing GRU with SVM, a 50% increase in accuracy is observed with a 95% error of 0.85 m for GRU compared to 1.78 m for SVM

A Robust GNSS tracking enhancement for hostile environments

openIn urban environments any GNSS receiver is subjected to frequent sudden losses of the line-of-sight (LOS) signal and to the multipath phenomenon, which drastically reduce the accuracy, robustness and availability of the GNSS service. This thesis evaluates the implementation of a tracking loop for robust tracking of GNSS signals in hostile scenarios, developed in collaboration with Qascom and addressed to their software defined GNSS receiver, the QN400. After an initial analysis of the state of the art in robustness enhancement techniques, it was decided, together with Qascom's Advanced Navigation team, to integrate a Kalman filter inside the common tracking loop structure. More specifically, the proposed tracking loop integrates a fourth-order Kalman filter and an outage detection algorithm into the standard structure, with the overall goal of improving tracking performance in terms of robustness to multipath effects and signal's interruptions. The proposed design was extensively tested with Qascom's semi-analytical simulator in Matlab, both with simulated scenarios, based on the DLR land mobile multipath channel model, and more realistic ones based on live recordings of a GNSS receiver mounted on a vehicle moving in Bassano del Grappa. The proposed solution has shown great efficacy in all designed test environments. Specifically, it has demonstrated superior resilience in resisting signal outages when compared to the standard tracking loop

Performance enhancement of low-cost INS/GNSS navigation system operating in urban environments

As a result of the increasing usage of UAVs (Unmanned Air Vehicles) in urban environments for UAM (Urban Air Mobility) applications, the preciseness and reliability of PNT (Positioning, Navigation and Timing) systems have critical importance for mission safety and success. With its high accuracy and global coverage, GNSS (Global Navigation Satellite System) is the primary PNT source for UAM applications. However, GNSS is highly vulnerable to Non-Line-of-Sight (NLoS) blockages and multipath (MP) reflections, which are quite common, especially in urban areas. This study proposes a machine learning-based NLoS/MP detection and exclusion algorithm using GNSS observables to enhance position estimations at the receiver level. By using the ensemble machine learning algorithm with the proposed method, overall 93.2% NLoS/MP detection accuracy was obtained, and 29.8% accuracy enhancement was achieved by excluding these detected signals

GNSS Vulnerabilities and Existing Solutions:A Review of the Literature

This literature review paper focuses on existing vulnerabilities associated with global navigation satellite systems (GNSSs). With respect to the civilian/non encrypted GNSSs, they are employed for proving positioning, navigation and timing (PNT) solutions across a wide range of industries. Some of these include electric power grids, stock exchange systems, cellular communications, agriculture, unmanned aerial systems and intelligent transportation systems. In this survey paper, physical degradations, existing threats and solutions adopted in academia and industry are presented. In regards to GNSS threats, jamming and spoofing attacks as well as detection techniques adopted in the literature are surveyed and summarized. Also discussed are multipath propagation in GNSS and non line-of-sight (NLoS) detection techniques. The review also identifies and discusses open research areas and techniques which can be investigated for the purpose of enhancing the robustness of GNSS

Analysing the effects of sensor fusion, maps and trust models on autonomous vehicle satellite navigation positioning

This thesis analyzes the effects of maps, sensor fusion and trust models on autonomous vehicle satellite positioning. The aim is to analyze the localization improvements that commonly used sensors, technologies and techniques provide to autonomous vehicle positioning. This thesis includes both survey of localization techniques used by other research and their localization accuracy results as well as experimentation where the effects of different technologies and techniques on lateral position accuracy are reviewed. The requirements for safe autonomous driving are strict and while the performance of the average global navigation satellite system (GNSS) receiver alone may not prove to be adequate enough for accurate positioning, it may still provide valuable position data to an autonomous vehicle. For the vehicle, this position data may provide valuable information about the absolute position on the globe, it may improve localization accuracy through sensor fusion and it may act as an independent data source for sensor trust evaluation. Through empirical experimentation, the effects of sensor fusion and trust functions with an inertial measurement unit (IMU) on GNSS lateral position accuracy are measured and analyzed. The experimentation includes the measurements from both consumer-grade devices mounted on a traditional automobile and high-end devices of a truck that is capable of autonomous driving in a monitored environment. The maps and LIDAR measurements used in the experiments are prone to errors and are taken into account in the analysis of the data