Assessment of the Multipath Mitigation Effect of Vector Tracking in an Urban Environment

Today, smart mobiles play an important role in our daily life. Most of these devices are equipped with a navigation function based on GNSS positioning. However, these devices may not work accurately in urban environments due to severe multipath interference and non-line of sight (NLOS) reception caused by nearby buildings. A promising approach for reducing the effect of multipath interference and NLOS reception is vector tracking (VT). VT is well-known for its robustness against poor signal-to-noise levels. However, its capability against multipath and NLOS has yet to be determined. The new combination of this paper is therefore to evaluate the performance of vector tracking in the presence of multipath and NLOS effects. A vector delay lock loop (VDLL) is used as the vector tracking technique. The noise tuning of the extended Kalman filter (EKF) in vector tracking is a key factor affecting its performance. Therefore, developed an adaptive noise tuning algorithm had been based on the measurement innovation. In order to evaluate vector tracking’s performance, equivalent conventional tracking loops are used as a control. GNSS signals were collected, while walking around in a challenging urban environment subject to multipath interference. The experimental results show that VT generates a more stable code numerical-controlled oscillator (NCO) frequency than CT does. This characteristic could reduce the impact of multipath interference which is reflected in a smaller position error using VT during most of run. To further test capability of VT against signal attenuation, this paper applies a signal cancellation method called direct signal cancellation algorithm to simulate the scenario of signal termination and NLOS reception. According to the simulation, VT provides not only robustness against signal termination but can also detect NLOS reception without any external aiding

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

Advanced Tracking Loop Architectures for Multi-frequency GNSS Receiver

The multi-frequency Global Navigation Satellite System (GNSS) signals are designed to overcome the inherent performance limitations of single-frequency receivers. However, the processing of multiple frequency signals in a time-varying GNSS signal environment which are potentially affected by multipath, ionosphere scintillation, blockage, and interference is quite challenging, as each signal is influenced differently by channel effects according to its Radio Frequency (RF). In order to get benefit of synchronously/coherently generated multiple frequency signals, advanced receiver signal processing techniques need to be developed.The aim of this research thesis is to extract the best performance benefits out of multifrequency GNSS signals in a time-varying GNSS signal environment. To accomplish this objective, it is necessary to analyze the multi-frequency signal characteristics and to investigate suitable signal processing algorithms in order to enable the best performance of each signal. The GNSS receiver position accuracy and reliability are majorly determined by the signal tracking-loop performance, hence, the primary focus of this thesis is on improving the tracking-loop performance of coherently generated multi-frequency signals.In the first phase of this research, the performance of multi-frequency GNSS signals is analyzed using conventional signal processing algorithms. Furthermore, the performance of a combination of multi-frequency signals is evaluated in order to find the optimum two-frequency signal combination for standalone and differential positioning applications. The limitations of the conventional multi-frequency signal processing algorithms are identified and an optimum dual-frequency signal processing architecture is proposed for robust and precise positioning applications.By making use of the inherent linear relation between the Line-of-Sight (LOS) Doppler shifts of multi-frequency GNSS signals, a computationally efficient Centralized Dynamics Tracking Loop (CTL) architecture is also proposed. In the CTL architecture, the common geometric Doppler shift in the received multi-frequency signals is estimated using a higher-order wide-band filter by making use of multiple frequency channel measurements in a coordinated manner. Additionally, the residual-phase variations specific to each frequency channel are tracked using Phase Lock Loop (PLL) with a narrow bandwidth filter. The CTL filter provides the geometric Doppler shift aid to individual frequency channels. The common Doppler-aided narrow-band signal tracking enhances the signal tracking sensitivity and robustness to the in-band interference in each frequency channel. This further reduces the noise in the linear combination of pseudorange observations.In real GNSS signal environment, multiple frequency signals are often subjected to intentional or unintentional RF interference either at the same time or at different time instants. Moreover, each of these signals is influenced differently by RF interference. To track signals in such time-varying signal conditions, the CTL using an Adaptive Kalman Filter (AKF) is proposed to enable an adaptive tracking loop bandwidth in response to received signal power level and signal dynamics. The central task of the AKF is to effectively blend multiple frequency carrier-phase observations to estimate the common geometric Doppler frequency of received multiple frequency signals. A suitable collaboration in multi-frequency channel tracking using centralized dynamics tracking loop enables a robust carrier tracking even if some of the frequency channels are affected by ionospheric scintillation, multipath, or interference.The performance of the proposed multi-frequency GNSS signal processing algorithms is demonstrated using analytical methods and experimental results based on live satellite data collected over GPS L1, L2C, and L5 signal frequencies. The dual-frequency signal processing architecture proposed in this research thesis has reduced the position error by 50%. The centralized dynamics multi-frequency carrier tracking loop has enhanced the individual channel tracking loop threshold by 7 dB in challenging signal conditions

Survey on Recent Advances in Integrated GNSSs Towards Seamless Navigation Using Multi-Sensor Fusion Technology

During the past few decades, the presence of global navigation satellite systems (GNSSs) such as GPS, GLONASS, Beidou and Galileo has facilitated positioning, navigation and timing (PNT) for various outdoor applications. With the rapid increase in the number of orbiting satellites per GNSS, enhancements in the satellite-based augmentation systems (SBASs) such as EGNOS and WAAS, as well as commissioning new GNSS constellations, the PNT capabilities are maximized to reach new frontiers. Additionally, the recent developments in precise point positioning (PPP) and real time kinematic (RTK) algorithms have provided more feasibility to carrier-phase precision positioning solutions up to the third-dimensional localization. With the rapid growth of internet of things (IoT) applications, seamless navigation becomes very crucial for numerous PNT dependent applications especially in sensitive fields such as safety and industrial applications. Throughout the years, GNSSs have maintained sufficiently acceptable performance in PNT, in RTK and PPP applications however GNSS experienced major challenges in some complicated signal environments. In many scenarios, GNSS signal suffers deterioration due to multipath fading and attenuation in densely obscured environments that comprise stout obstructions. Recently, there has been a growing demand e.g. in the autonomous-things domain in adopting reliable systems that accurately estimate position, velocity and time (PVT) observables. Such demand in many applications also facilitates the retrieval of information about the six degrees of freedom (6-DOF - x, y, z, roll, pitch, and heading) movements of the target anchors. Numerous modern applications are regarded as beneficiaries of precise PNT solutions such as the unmanned aerial vehicles (UAV), the automatic guided vehicles (AGV) and the intelligent transportation system (ITS). Hence, multi-sensor fusion technology has become very vital in seamless navigation systems owing to its complementary capabilities to GNSSs. Fusion-based positioning in multi-sensor technology comprises the use of multiple sensors measurements for further refinement in addition to the primary GNSS, which results in high precision and less erroneous localization. Inertial navigation systems (INSs) and their inertial measurement units (IMUs) are the most commonly used technologies for augmenting GNSS in multi-sensor integrated systems. In this article, we survey the most recent literature on multi-sensor GNSS technology for seamless navigation. We provide an overall perspective for the advantages, the challenges and the recent developments of the fusion-based GNSS navigation realm as well as analyze the gap between scientific advances and commercial offerings. INS/GNSS and IMU/GNSS systems have proven to be very reliable in GNSS-denied environments where satellite signal degradation is at its peak, that is why both integrated systems are very abundant in the relevant literature. In addition, the light detection and ranging (LiDAR) systems are widely adopted in the literature for its capability to provide 6-DOF to mobile vehicles and autonomous robots. LiDARs are very accurate systems however they are not suitable for low-cost positioning due to the expensive initial costs. Moreover, several other techniques from the radio frequency (RF) spectrum are utilized as multi-sensor systems such as cellular networks, WiFi, ultra-wideband (UWB) and Bluetooth. The cellular-based systems are very suitable for outdoor navigation applications while WiFi-based, UWB-based and Bluetooth-based systems are efficient in indoor positioning systems (IPS). However, to achieve reliable PVT estimations in multi-sensor GNSS navigation, optimal algorithms should be developed to mitigate the estimation errors resulting from non-line-of-sight (NLOS) GNSS situations. Examples of the most commonly used algorithms for trilateration-based positioning are Kalman filters, weighted least square (WLS), particle filters (PF) and many other hybrid algorithms by mixing one or more algorithms together. In this paper, the reviewed articles under study and comparison are presented by highlighting their motivation, the methodology of implementation, the modelling utilized and the performed experiments. Then they are assessed with respect to the published results focusing on achieved accuracy, robustness and overall implementation cost-benefits as performance metrics. Our summarizing survey assesses the most promising, highly ranked and recent articles that comprise insights into the future of GNSS technology with multi-sensor fusion technique.©2021 The Authors. Published by ION.fi=vertaisarvioimaton|en=nonPeerReviewed

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

A Survey of Positioning Systems Using Visible LED Lights

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.As Global Positioning System (GPS) cannot provide satisfying performance in indoor environments, indoor positioning technology, which utilizes indoor wireless signals instead of GPS signals, has grown rapidly in recent years. Meanwhile, visible light communication (VLC) using light devices such as light emitting diodes (LEDs) has been deemed to be a promising candidate in the heterogeneous wireless networks that may collaborate with radio frequencies (RF) wireless networks. In particular, light-fidelity has a great potential for deployment in future indoor environments because of its high throughput and security advantages. This paper provides a comprehensive study of a novel positioning technology based on visible white LED lights, which has attracted much attention from both academia and industry. The essential characteristics and principles of this system are deeply discussed, and relevant positioning algorithms and designs are classified and elaborated. This paper undertakes a thorough investigation into current LED-based indoor positioning systems and compares their performance through many aspects, such as test environment, accuracy, and cost. It presents indoor hybrid positioning systems among VLC and other systems (e.g., inertial sensors and RF systems). We also review and classify outdoor VLC positioning applications for the first time. Finally, this paper surveys major advances as well as open issues, challenges, and future research directions in VLC positioning systems.Peer reviewe

PRECISE KINEMATIC APPLICATIONS OF GPS: PROSPECTS AND CHALLENGES

GPS kinematic positioning in the post-processed or in the real-time mode is now increasingly used for many surveying and navigation applications on land, at sea and in the air. Techniques range from the robust pseudo-range-based differential GPS (DGPS) techniques capable of delivering accuracies at the few metre level, to sophisticated carrier phase-based centimetre accuracy techniques. The distance from the mobile receiver to the nearest reference receiver may range from a few kilometres to hundreds of kilometres. As the receiver separation increases, the problems of accounting for distance-dependent biases grows. For carrier phasebased techniques reliable ambiguity resolution becomes an even greater challenge. In the case of DGPS, more appropriate implementations such as Wide Area DGPS become necessary. In this paper, the challenges, progress and outlook for high precision GPS kinematic positioning for the short-range, medium-range and long-range cases, in both the post-processing and real-time modes will be discussed. Although the focus will be on carrier phase-based systems, some comments will also be made with regards to DGPS systems. Several applications of kinematic GPS positioning will be considered, so as to demonstrate the engineering challenges in addition to GPS, that have to be addressed

Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods