Implementation and Performance of a GPS/INS Tightly Coupled Assisted PLL Architecture Using MEMS Inertial Sensors

The use of global navigation satellite system receivers for navigation still presents many challenges in urban canyon and indoor environments, where satellite availability is typically reduced and received signals are attenuated. To improve the navigation performance in such environments, several enhancement methods can be implemented. For instance, external aid provided through coupling with other sensors has proven to contribute substantially to enhancing navigation performance and robustness. Within this context, coupling a very simple GPS receiver with an Inertial Navigation System (INS) based on low-cost micro-electro-mechanical systems (MEMS) inertial sensors is considered in this paper. In particular, we propose a GPS/INS Tightly Coupled Assisted PLL (TCAPLL) architecture, and present most of the associated challenges that need to be addressed when dealing with very-low-performance MEMS inertial sensors. In addition, we propose a data monitoring system in charge of checking the quality of the measurement flow in the architecture. The implementation of the TCAPLL is discussed in detail, and its performance under different scenarios is assessed. Finally, the architecture is evaluated through a test campaign using a vehicle that is driven in urban environments, with the purpose of highlighting the pros and cons of combining MEMS inertial sensors with GPS over GPS alone

Positioning Based on Tightly Coupled Multiple Sensors: A Practical Implementation and Experimental Assessment

During the last decade, the number of applications for land transportation that depend on systems for accurate positioning has significantly increased. Unfortunately, systems based on low-cost global navigation satellite system (GNSS) components harshly suffer signal impairments due to the environment surrounding the antenna, but new designs based on deeper data fusion and on the combination of different signal processing techniques can overcome limitations without the introduction of expensive components. Supported by a complete mathematical model, this paper presents the design of a real-time positioning system that is based on the tight integration of extremely low-cost sensors and a consumer-grade global positioning system receiver. The design has been validated experimentally through a series of tests carried out in real scenarios. The performance of the new system is compared against a standalone GNSS receiver and survey-grade professional equipment. The results show that a carefully designed and constrained integration of low-cost sensors can have performance comparable to that of an expensive professional equipment

Kinematic State Estimation using Multiple DGPS/MEMS-IMU Sensors

Animals have evolved over billions of years and understanding these complex and intertwined systems have potential to advance the technology in the field of sports science, robotics and more. As such, a gait analysis using Motion Capture (MOCAP) technology is the subject of a number of research and development projects aimed at obtaining quantitative measurements. Existing MOCAP technology has limited the majority of studies to the analysis of the steady-state locomotion in a controlled (indoor) laboratory environment. MOCAP systems such as the optical, non-optical acoustic and non-optical magnetic MOCAP systems require predefined capture volume and controlled environmental conditions whilst the non-optical mechanical MOCAP system impedes the motion of the subject. Although the non-optical inertial MOCAP system allows MOCAP in an outdoor environment, it suffers from measurement noise and drift and lacks global trajectory information. The accuracy of these MOCAP systems are known to decrease during the tracking of the transient locomotion. Quantifying the manoeuvrability of animals in their natural habitat to answer the question “Why are animals so manoeuvrable?” remains a challenge. This research aims to develop an outdoor MOCAP system that will allow tracking of the steady-state as well as the transient locomotion of an animal in its natural habitat outside a controlled laboratory condition. A number of researchers have developed novel MOCAP systems with the same aim of creating an outdoor MOCAP system that is aimed at tracking the motion outside a controlled laboratory (indoor) environment with unlimited capture volume. These novel MOCAP systems are either not validated against the commercial MOCAP systems or do not have comparable sub-millimetre accuracy as the commercial MOCAP systems. The developed DGPS/MEMS-IMU multi-receiver fusion MOCAP system was assessed to have global trajectory accuracy of _0:0394m, relative limb position accuracy of _0:006497m. To conclude the research, several recommendations are made to improve the developed MOCAP system and to prepare for a field-testing with a wild animal from a family of a terrestrial megafauna

Error characteristics of a model-based integration approach for fixed-wing unmanned aerial vehicles

The paper presents the error characteristics of a vehicle dynamic model (VDM)-based integration architecture for fixed-wing unmanned aerial vehicles. Global navigation satellite system (GNSS) and inertial measurement unit measurements are fused in an extended Kalman filter (EKF) which uses the VDM as the main process model. Control inputs from the autopilot system are used to drive the navigation solution. Using a predefined trajectory with segments of both high and low dynamics and a variable wind profile, Monte Carlo simulations reveal a degrading performance in varying periods of GNSS outage lasting 10 s, 20 s, 30 s, 60 s and 90 s, respectively. These are followed by periods of re-acquisition where the navigation solution recovers. With a GNSS outage lasting less than 60 s, the position error gradually grows to a maximum of 8⋅4 m while attitude errors in roll and pitch remain bounded, as opposed to an inertial navigation system (INS)/GNSS approach in which the navigation solution degrades rapidly. The model-based approach shows improved navigation performance even with parameter uncertainties over a conventional INS/GNSS integration approach

Safe navigation for vehicles

La navigation par satellite prend un virage très important ces dernières années, d'une part par l'arrivée imminente du système Européen GALILEO qui viendra compléter le GPS Américain, mais aussi et surtout par le succès grand public qu'il connaît aujourd'hui. Ce succès est dû en partie aux avancées technologiques au niveau récepteur, qui, tout en autorisant une miniaturisation de plus en plus avancée, en permettent une utilisation dans des environnements de plus en plus difficiles. L'objectif aujourd'hui est de préparer l'utilisation de ce genre de signal dans une optique bas coût dans un milieu urbain automobile pour des applications critiques d'un point de vue sécurité (ce que ne permet pas les techniques d'hybridation classiques). L'amélioration des technologies (réduction de taille des capteurs type MEMS ou Gyroscope) ne peut, à elle seule, atteindre l'objectif d'obtenir une position dont nous pouvons être sûrs si nous utilisons les algorithmes classiques de localisation et d'hybridation. En effet ces techniques permettent d'avoir une position sans cependant permettre d'en quantifier le niveau de confiance. La faisabilité de ces applications repose d'une part sur une recherche approfondie d'axes d'amélioration des algorithmes de localisation, mais aussi et conjointement, sur la possibilité, via les capteurs externes de maintenir un niveau de confiance élevé et quantifié dans la position même en absence de signal satellitaire. ABSTRACT : Satellite navigation has acquired an increased importance during these last years, on the one hand due to the imminent appearance of the European GALILEO system that will complement the American GPS, and on the other hand due to the great success it has encountered in the commercial civil market. An important part of this success is based on the technological development at the receiver level that has rendered satellite navigation possible even in difficult environments. Today's objective is to prepare the utilisation of this kind of signals for land vehicle applications demanding high precision positioning. One of the main challenges within this research domain, which cannot be addressed by classical coupling techniques, is related to the system capability to provide reliable position estimations. The enhancement in dead-reckoning technologies (i.e. size reduction of MEMS-based sensors or gyroscopes) cannot all by itself reach the necessary confidence levels if exploited with classical localization and integration algorithms. Indeed, these techniques provide a position estimation whose reliability or confidence level it is very difficult to quantify. The feasibility of these applications relies not only on an extensive research to enhance the navigation algorithm performances in harsh scenarios, but also and in parallel, on the possibility to maintain, thanks to the presence of additional sensors, a high confidence level on the position estimation even in the absence of satellite navigation signals

Robust localization with wearable sensors

Measuring physical movements of humans and understanding human behaviour is useful in a variety of areas and disciplines. Human inertial tracking is a method that can be leveraged for monitoring complex actions that emerge from interactions between human actors and their environment. An accurate estimation of motion trajectories can support new approaches to pedestrian navigation, emergency rescue, athlete management, and medicine. However, tracking with wearable inertial sensors has several problems that need to be overcome, such as the low accuracy of consumer-grade inertial measurement units (IMUs), the error accumulation problem in long-term tracking, and the artefacts generated by movements that are less common. This thesis focusses on measuring human movements with wearable head-mounted sensors to accurately estimate the physical location of a person over time. The research consisted of (i) providing an overview of the current state of research for inertial tracking with wearable sensors, (ii) investigating the performance of new tracking algorithms that combine sensor fusion and data-driven machine learning, (iii) eliminating the effect of random head motion during tracking, (iv) creating robust long-term tracking systems with a Bayesian neural network and sequential Monte Carlo method, and (v) verifying that the system can be applied with changing modes of behaviour, defined as natural transitions from walking to running and vice versa. This research introduces a new system for inertial tracking with head-mounted sensors (which can be placed in, e.g. helmets, caps, or glasses). This technology can be used for long-term positional tracking to explore complex behaviours

Contributions to Positioning Methods on Low-Cost Devices

Global Navigation Satellite System (GNSS) receivers are common in modern consumer devices that make use of position information, e.g., smartphones and personal navigation assistants. With a GNSS receiver, a position solution with an accuracy in the order of five meters is usually available if the reception conditions are benign, but the performance degrades rapidly in less favorable environments and, on the other hand, a better accuracy would be beneficial in some applications. This thesis studies advanced methods for processing the measurements of low-cost devices that can be used for improving the positioning performance. The focus is on GNSS receivers and microelectromechanical (MEMS) inertial sensors which have become common in mobile devices such as smartphones. First, methods to compensate for the additive bias of a MEMS gyroscope are investigated. Both physical slewing of the sensor and mathematical modeling of the bias instability process are considered. The use of MEMS inertial sensors for pedestrian navigation indoors is studied in the context of map matching using a particle filter. A high-sensitivity GNSS receiver is used to produce coarse initialization information for the filter to decrease the computational burden without the need to exploit local building infrastructure. Finally, a cycle slip detection scheme for stand-alone single-frequency GNSS receivers is proposed. Experimental results show that even a MEMS gyroscope can reach an accuracy suitable for North seeking if the measurement errors are carefully modeled and eliminated. Furthermore, it is seen that even a relatively coarse initialization can be adequate for long-term indoor navigation without an excessive computational burden if a detailed map is available. The cycle slip detection results suggest that even small cycle slips can be detected with mass-market GNSS receivers, but the detection rate needs to be improved