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

UAS Model Identification and Simulation to Support In-Flight Testing of Discrete Adaptive Fault-Tolerant Control Laws

In mission-critical applications of unmanned and autonomous aerial systems(UAS), it is of significant importance to develop robust strategies for fault-tolerant systems that can countermeasure system degradation and consequently support the integration into the National Airspace (NAS). This thesis research illustrates the results of systems identification that is performed using DATCOM followed by the flight test data. This data is acquired from conducting an intensive flight testings program of a fixed-wing UAS to determine the state-space model of the aircraft. A discrete state-space system is reconstructed from these models to derive Auto-Regressive Moving-Average (ARMA) models used to design a Discrete Direct and Indirect Model Reference Adaptive Control. Description of the UAS, sub-systems, and integration is presented in this thesis along with analysis of results from numerical simulation to support the design, development, and validation of adaptive control laws for fault tolerance. A set of performance metrics are defined to perform the analysis in terms of control effort, tracking performance, and reconfiguration of control laws under commonly occurring failures such as partial control surface damage, pilot-induced oscillations, and uncertain ice accretion

Cyber-Physical Systems Enabled By Unmanned Aerial System-Based Personal Remote Sensing: Data Mission Quality-Centric Design Architectures

In the coming 20 years, unmanned aerial data collection will be of great importance to many sectors of civilian life. Of these systems, Personal Remote Sensing (PRS) Small Unmanned Aerial Systems (sUASs), which are designed for scientic data collection, will need special attention due to their low cost and high value for farming, scientic, and search-andrescue uses, among countless others. Cyber-Physical Systems (CPSs: large-scale, pervasive automated systems that tightly couple sensing and actuation through technology and the environment) can use sUASs as sensors and actuators, leading to even greater possibilities for benet from sUASs. However, this nascent robotic technology presents as many problems as possibilities due to the challenges surrounding the abilities of these systems to perform safely and eectively for personal, academic, and business use. For these systems, whose missions are dened by the data they are sent to collect, safe and reliable mission quality is of highest importance. Much like the dawning of civil manned aviation, civilian sUAS ights demand privacy, accountability, and other ethical factors for societal integration, while safety of the civilian National Airspace (NAS) is always of utmost importance. While the growing popularity of this technology will drive a great effort to integrate sUASs into the NAS, the only long-term solution to this integration problem is one of proper architecture. In this research, a set of architectural requirements for this integration is presented: the Architecture for Ethical Aerial Information Sensing or AERIS. AERIS provides a cohesive set of requirements for any architecture or set of architectures designed for safe, ethical, accurate aerial data collection. In addition to an overview and showcase of possibilities for sUAS-enabled CPSs, specific examples of AERIS-compatible sUAS architectures using various aerospace design methods are shown. Technical contributions include specic improvements to sUAS payload architecture and control software, inertial navigation and complementary lters, and online energy and health state estimation for lithium-polymer batteries in sUAS missions. Several existing sUASs are proled for their ability to comply with AERIS, and the possibilities of AERIS data-driven missions overall is addressed

Anti-disturbance fault tolerant initial alignment for inertial navigation system subjected to multiple disturbances

Modeling error, stochastic error of inertial sensor, measurement noise and environmental disturbance affect the accuracy of an inertial navigation system (INS). In addition, some unpredictable factors, such as system fault, directly affect the reliability of INSs. This paper proposes a new anti-disturbance fault tolerant alignment approach for a class of INSs sub- jected to multiple disturbances and system faults. Based on modeling and error analysis, stochastic error of inertial sensor, measurement noise, modeling error and environmental disturbance are formulated into different types of disturbances described by a Markov stochastic process, Gaussian noise and a norm-bounded variable, respectively. In order to improve the accuracy and reliability of an INS, an anti-disturbance fault tolerant filter is designed. Then, a mixed dissipative/guarantee cost performance is applied to attenuate the norm-bounded disturbance and to optimize the estimation error. Slack variables and dissipativeness are introduced to reduce the conservatism of the proposed approach. Finally, compared with the unscented Kalman filter (UKF), simulation results for self-alignment of an INS are provided based on experimental data. It can be shown that the proposed method has an enhanced disturbance rejection and attenuation performance with high reliability

Design and Implementation of Intelligent Guidance Algorithms for UAV Mission Protection

In recent years, the interest of investigating intelligent systems for Unmanned Aerial Vehicles (UAVs) have increased in popularity due to their large range of capabilities such as on-line obstacle avoidance, autonomy, search and rescue, fast prototyping and integration in the National Air Space (NAS). Many research efforts currently focus on system robustness against uncertainties but do not consider the probability of readjusting tasks based on the remaining resources to successfully complete the mission. In this thesis, an intelligent algorithm approach is proposed along with decision-making capabilities to enhance UAVs post-failure performance. This intelligent algorithm integrates a set of path planning algorithms, a health monitoring system and a power estimation approach. Post-fault conditions are considered as unknown uncertainties that unmanned vehicles could encounter during regular operation missions. In this thesis, three main threats are studied: the presence of unknown obstacles in the environment, sub-system failures, and low power resources. A solution for adapting to new circumstances is addressed by enabling autonomous decision-making and re-planning capabilities in real time