Due to their complementary characteristics, Global Positioning System (GPS) is usually integrated with standalone navigation devices like odometers and inertial measurement units (IMU). Recently, intensive research has focused on utilizing Micro-Electro-Mechanical-System (MEMS) grade inertial sensors in the integration because of their low cost. In this study, a reduced inertial sensor system (RISS) is considered. It comprises a MEMS grade single axis gyroscope, the vehicle built-in odometer, and two optional MEMS grade accelerometers. Estimation technique is needed to allow the data fusion of RISS and GPS. With adequate accuracy, Kalman filter (KF) fulfills this requirement if high-end inertial sensors are used. However, due to the inherent error characteristics of MEMS devices, MEMS-based RISS suffers from the non-stationary stochastic sensor errors and nonlinear inertial errors, which cannot be suppressed by KF alone. To solve the problem, Fast Orthogonal Search (FOS), a nonlinear system identification algorithm, is suggested in this research for modeling higher order RISS errors. FOS algorithm has the ability to figure out the system nonlinearity with a tolerance of arbitrary stochastic system noise. Its modeling results can then be used to predict the system dynamics. Motivated by the above merits, a KF/FOS module is proposed. By handling both linear and nonlinear RISS errors, this module targets substantial enhancement of positioning accuracy. To examine the effectiveness of the proposed technique, KF/FOS module is applied on RISS with GPS in a land vehicle for several road test trajectories. Its performance is compared to KF-only method, both assessed with respect to a high-end reference. To evaluate navigation algorithm in real-time vehicle application, a multi-sensor data logger is designed in this research to collect online RISS/GPS data. KF/FOS module is transplanted on an embedded digital signal processor as well. Both the off-line and online results confirm that KF/FOS module outperforms KF-only approach in positioning accuracy. They also demonstrate reliable real-time performance. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2012-01-22 01:26:11.477

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This dissertation addresses path planning for an autonomous vehicle navigating in a two dimensional environment for which an a priori map is inaccurate and for which the environment is sensed in real-time. For this class of application, planning decisions must be made in real-time. This work is motivated by the need for fast autonomous vehicles that require planning algorithms to operate as quickly as possible. In this dissertation, we first study the case in which there are only static obstacles in the environment. We propose a hybrid receding horizon control path planning algorithm that is based on level-set methods. The hybrid method uses global or local level sets in the formulation of the receding horizon control problem. The decision to select a new level set is made based on certain matching conditions that guarantee the optimality of the path. We rigorously prove sufficient conditions that guarantee that the vehicle will converge to the goal as long as a path to the goal exists. We then extend the proposed receding horizon formulation to the case when the environment possesses moving obstacles. Since all of the results in this dissertation are based on level-set methods, we rigorously investigate how level sets change in response to new information locally sensed by a vehicle. The result is a dynamic fast marching algorithm that usually requires significantly less computation that would otherwise be the case. We demonstrate the proposed dynamic fast marching method in a successful field trial for which an autonomous surface vehicle navigated four kilometers through a riverine environment. / Ph. D.

A human factors experiment was conducted to investigate the potential benefits of using directional auditory cues in intelligent transportation system technologies in commercial vehicles. Twelve licensed commercial vehicle operators drove a commercial truck-driving simulator and were prompted to select highway numbers on a side-task display. Prompts were presented visually or aurally. Auditory prompts were presented either diotically (both ears simultaneously) or directionally (to either the left or right ear). The search task varied in map density and timing of the prompts in relation to speed limit changes. All experimental conditions were compared to a control condition containing no secondary task. Both driving performance (lane deviation, steering wheel angle, road heading angle error, accidents, and adherence to the speed limit) and secondary task performance (accuracy and response time) measures were collected. Results showed that drivers were able to respond more quickly and accurately to the search task when directional auditory cues were used. Results also showed that driving performance degrades when display density increases and that the use of directional auditory prompts lessens this deterioration of performance for high-density conditions. / Master of Science

Dans les systèmes de positionnement de véhicules, à tout moment, n'importe lequel des détecteurs peut, temporairement ou de manière permanente, tomber en panne ou cesser d'envoyer des informations. Il s'ensuit alors des répercussions sur la sécurité, la santé, ainsi que des informations financières ou même légales. Bien que les nouvelles pratiques de conception aient tendance à réduire au minimum les défaillances des détecteurs, il est reconnu que de tels évènements peuvent quand même souvenir. Dans un tel cas, le détecteur défectueux doit être identifié et isolé afin d'éviter de corrompre les évaluations globales et, finalement, le système doit être capable de se reconfigurer afin de surmonter le carence causée par la défaillance. En bref, un système de navigation doit être robuste et adaptatif. Cette thèse propose plusieurs architectures de fusion de données capables de s'adapter suite à des défaillances de détecteurs. Les diverses approches utilisent un filtre Kalman en combinaison avec la détection de défauts pour produire des modules de positionnement robuste. Les modules devront être capables de fonctionner dans des situations telles que l'entrée GPS est corrompue ou non disponible, ou bien qu'un plusieurs détecteurs de position sont défectueux ou bloqués. Le principe de travail vise la modification des gains du filtre Kalman en se basant sur les erreurs normalisées entre les états estimés et les observations. Pour évaluer l'architecture proposée, divers défauts de détecteurs et diverses dégradations de performance ont été mis en oeuvre et simulés. Les expériences démontrent que les solutions proposées peuvent compenser la plupart des erreurs associées aux défauts des détecteurs ou aux dégradations de performance, et que l'exactitude de positionnement qui en découle est améliorée significativement.

<p>In spite of all electronic navigation devices on a modern ship bridge, navigators still lose their orientation. Reasons for this might be excessive cognitive workload caused by too many instruments to read and compile, navigation information that is displayed in a cognitively demanding way, short decision times due to high speed or fatigue due to minimum manning and long work hours.</p><p>This work addresses the problem of map information displayed in a less than optimal way. Three new concepts are presented: the bridge perspective, the NoGO area polygons and a dual lane seaway network. Map reading can be difficult due to the problem of mental rotations. By allowing a 3-D nautical chart to be viewed from an egocentric bridge perspective, the need for mental rotations can be removed. The cognitively demanding calculations necessary to find out if there is enough water under the keel can be made by the chart system and the result displayed as of free water and NoGo areas. On land car driving is facilitated by a road-network and a sign system. This notion can be further developed on sea and make navigation easier and safer.</p><p>These concepts were then tested in a laboratory experiment, in interviews and in a prototyping project. The results were very promising. The experiment in a laboratory maze showed that map reading from an egocentric perspective was more efficient than using traditional paper and electronic maps. Interviews and expert evaluation of prototypes also showed great interest from practitioners in the field.</p> / <p>Trots all elektronisk utrustning på en modern skeppsbrygga händer det att navigatörerna förlorar orienteringen. Anledningen kan vara hög kognitiv belastning därför att för många olika instrument måste avläsas och integreras samtidigt, att informationen på instrumenten behöver tolkas på ett kognitivt krävande sätt, att tiden för att fatta beslut blir allt kortare på grund av högre hastigheter till sjöss eller på grund av trötthet.</p><p>I detta arbete presenteras tre nya koncept för visualisering av navigationsinformation: bryggperspektivet, djupvarningspolygoner och sjövägar.</p><p>Kartläsning kan ibland vara svårt på grund av de mentala rotationer en användare tvingas genomföra för att kunna jämföra kartan med verkligheten. Genom att göra det möjligt för en användare att se sjökortet ur ett egocentriskt bryggperspektiv, så onödiggörs dessa mentala rotationer. De kognitivt krävande beräkningar som navigatören behöver göra för att försäkra sig om att det finns tillräckligt med vatten under kölen, kan utföras av kartsystemet och resultatet visas istället som fria vattenytor och djupvarningsområden (NoGo areas). På land underlättas bilkörning av ett vägnät med körbanor, filer och skyltar. Detta system kan i högre utsträckning införas till sjöss för att underlätta säker navigering.</p><p>Dessa koncept har sedan testats genom ett laboratorieexperiment, genom intervjuer och i ett prototyputvecklingsprojekt. Resultaten var mycket lovande. Experimentet i en laboratorielabyrint visade klart att 3D-sjökortet var effektivare än både papperskartan och traditionell elektroniska kartor och intervjuerna och expertutvärderingarna visad på stort intresse från yrkesutövare i branschen.</p>

In spite of all electronic navigation devices on a modern ship bridge, navigators still lose their orientation. Reasons for this might be excessive cognitive workload caused by too many instruments to read and compile, navigation information that is displayed in a cognitively demanding way, short decision times due to high speed or fatigue due to minimum manning and long work hours. This work addresses the problem of map information displayed in a less than optimal way. Three new concepts are presented: the bridge perspective, the NoGO area polygons and a dual lane seaway network. Map reading can be difficult due to the problem of mental rotations. By allowing a 3-D nautical chart to be viewed from an egocentric bridge perspective, the need for mental rotations can be removed. The cognitively demanding calculations necessary to find out if there is enough water under the keel can be made by the chart system and the result displayed as of free water and NoGo areas. On land car driving is facilitated by a road-network and a sign system. This notion can be further developed on sea and make navigation easier and safer. These concepts were then tested in a laboratory experiment, in interviews and in a prototyping project. The results were very promising. The experiment in a laboratory maze showed that map reading from an egocentric perspective was more efficient than using traditional paper and electronic maps. Interviews and expert evaluation of prototypes also showed great interest from practitioners in the field. / Trots all elektronisk utrustning på en modern skeppsbrygga händer det att navigatörerna förlorar orienteringen. Anledningen kan vara hög kognitiv belastning därför att för många olika instrument måste avläsas och integreras samtidigt, att informationen på instrumenten behöver tolkas på ett kognitivt krävande sätt, att tiden för att fatta beslut blir allt kortare på grund av högre hastigheter till sjöss eller på grund av trötthet. I detta arbete presenteras tre nya koncept för visualisering av navigationsinformation: bryggperspektivet, djupvarningspolygoner och sjövägar. Kartläsning kan ibland vara svårt på grund av de mentala rotationer en användare tvingas genomföra för att kunna jämföra kartan med verkligheten. Genom att göra det möjligt för en användare att se sjökortet ur ett egocentriskt bryggperspektiv, så onödiggörs dessa mentala rotationer. De kognitivt krävande beräkningar som navigatören behöver göra för att försäkra sig om att det finns tillräckligt med vatten under kölen, kan utföras av kartsystemet och resultatet visas istället som fria vattenytor och djupvarningsområden (NoGo areas). På land underlättas bilkörning av ett vägnät med körbanor, filer och skyltar. Detta system kan i högre utsträckning införas till sjöss för att underlätta säker navigering. Dessa koncept har sedan testats genom ett laboratorieexperiment, genom intervjuer och i ett prototyputvecklingsprojekt. Resultaten var mycket lovande. Experimentet i en laboratorielabyrint visade klart att 3D-sjökortet var effektivare än både papperskartan och traditionell elektroniska kartor och intervjuerna och expertutvärderingarna visad på stort intresse från yrkesutövare i branschen.

Present land vehicle positioning and navigation relies mostly on the Global Positioning System (GPS). However, in urban canyons, tunnels, and other GPS-denied environments, the GPS positioning solution may be interrupted or suffer from deterioration in accuracy due to satellite signal blockage, poor satellite geometry or multipath effects. In order to achieve continuous positioning services, GPS is augmented with complementary systems capable of providing additional sources of positioning information, like inertial navigation systems (INS). Kalman filtering (KF) is traditionally used to provide integration of both INS and GPS utilizing linearized dynamic system and measurement models. Targeting low cost solution for land vehicles, Micro-Electro-Mechanical Systems (MEMS) based inertial sensors are used. Due to the inherent errors of MEMS inertial sensors and their stochastic nature, which is difficult to model, KF has limited capabilities in providing accurate positioning in challenging GPS environments. This research aims at developing reliable integrated navigation system capable of demonstrating accurate positioning during long periods of challenging GPS environments. Towards achieving this goal, Mixture Particle filtering (MPF) is suggested in this research as a nonlinear filtering technique for INS/GPS integration to accommodate arbitrary inertial sensor characteristics, motion dynamics and noise distributions. Since PF can accommodate nonlinear models, this research develops total-state nonlinear system and measurement models without any linearization, thus enabling reliable integrated navigation and mitigating one of the major drawbacks of KF. Exploiting the capabilities of PF, Parallel Cascade Identification (PCI), which is a nonlinear system identification technique, is used to obtain efficient stochastic models for inertial sensors instead of the currently utilized linear models, which are not adequate for MEMS-based sensors. Moreover, this research proposes a method to update the stochastic bias drift of inertial sensors from GPS data when the GPS signal is adequately received. Furthermore, a technique for automatic detection of GPS degraded performance is developed and led to improving the performance in urban canyons. The performance is examined using several road test experiments conducted in downtown cores to verify the adequacy and the benefits of the methods suggested. The results obtained demonstrate the superior performance of the proposed methods over conventional techniques. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2010-07-23 20:27:02.12

Rocket Factory Augsburg (RFA) a German New Space Startup is developing a three-stage rocket launcher aiming at LEO/SSO orbits. A fundamental responsibility of the GNC team is the development of the rocket navigation algorithm to estimate the attitude, position, and velocity allowing the guidance and control loops to autonomously steer the rocket. This thesis focuses on the analysis and design of a Hybrid Navigation system able to satisfy the various necessities of a launch vehicle, such as delay compensation and GNSS outages. The navigation architecture was chosen to be a Closed Loop, Loosely Coupled, Delayed Error State Kalman Filter thanks to the proven capability of COTS receivers to autonomously provide a consistent PVT solution throughout the flight. A preliminary analysis used a reference trajectory to evaluate the effect of the sensor grade on inertial performances and choose an appropriate integration scheme. The filter’s system model was explored using approximate analytical results on observability. The developed navigation module was then tested within a Monte Carlo simulation environment by perturbating the sensor parameter in accordance with the sensor datasheet. As a further verification, the modeled IMU output was compared to the engineering model, to assure that the simulation result would yield conservative errors. Due to concern over the visibility of GNSS satellites during flight, a simplified Almanac-based GPS model has been developed, proving that enough satellite visibility is available along the trajectory. The estimation error was compared with the filter’s estimated covariance and found well within the bounds. Through the study of the covariance evolution, it was determined that given the reference dynamics, the sensor misalignments are the least observable states. Realistic signal outages were introduced in the most critical flight intervals. The filter was indeed found to be robust and the tuning proved to be adequate to capture the dead reckoning drift. Finally, the entire navigation module was deployed onto the avionics engineering model, including the flight computer, IMU, GNSS, and antennas, in a configuration equivalent to flight. The navigation module was then tested to ensure that the execution was in performance under severe multipath errors and prolonged GNSS outages with the covariance estimates correctly covering the uncertainty. / Rocket Factory Augsburg (RFA), ett tyskt nystartat rymdföretag, utvecklar en trestegsraket som siktar på LEO/SSO-banor. Ett grundläggande ansvar för GNC-teamet är utvecklingen av raketnavigationsalgoritmen för att uppskatta attityd, position och hastighet så att styr- och kontrollslingorna kan styra raketen autonomt. Avhandlingen fokuserar på analys och design av ett hybridnavigeringssystem som kan uppfylla de olika krav som ställs på en bärraket, såsom kompensation för fördröjningar och GNSS-avbrott. Navigationsarkitekturen valdes att vara ett Closed Loop, Loosely Coupled, Delayed Error State Kalman Filter tack vare den bevisade förmågan hos COTS-mottagare att autonomt tillhandahålla en konsekvent PVT-lösning under hela flygningen. En preliminär analys använde en referensbana för att utvärdera effekten av sensorkvaliteten på tröghetsprestanda och välja ett lämpligt integrationsschema. Filtrets systemmodell undersöktes med hjälp av approximativa analytiska resultat om observerbarhet. Den utvecklade navigeringsmodulen testades sedan i en Monte Carlo-simuleringsmiljö genom att störa sensorparametern i enlighet med sensorns datablad. Som en ytterligare verifiering jämfördes den modellerade IMU-utgången med den tekniska modellen, för att säkerställa att simuleringsresultatet skulle ge konservativa fel. På grund av oro över GNSS-satelliternas synlighet under flygning har en förenklad Almanac-baserad GPS-modell utvecklats, som bevisar att tillräcklig satellitsikt finns tillgänglig längs banan. Uppskattningsfelet jämfördes med filtrets uppskattade kovarians och låg väl inom gränserna. Genom att studera kovariansutvecklingen fastställdes det att givet referensdynamiken är sensorernas feljusteringar de minst observerbara tillstånden. Realistiska signalavbrott infördes i de mest kritiska flygintervallen. Filtret visade sig verkligen vara robust och inställningen visade sig vara tillräcklig för att fånga upp dödberäkningens drift. Slutligen installerades hela navigeringsmodulen på den flygtekniska modellen, inklusive flygdator, IMU, GNSS och antenner, i en konfiguration som motsvarar en flygning. Navigationsmodulen testades sedan för att säkerställa att utförandet var i prestanda under allvarliga multipath-fel och långvariga GNSS-avbrott med kovariansuppskattningarna som korrekt täcker osäkerheten.

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