Navigation is about knowing your own position, orientation and velocity relative to some geographic entities. The sensor fusion considered in this thesis combines data from a dead reckoning system, inertial navigation system (INS), and measurements of the ground elevation. The very fast dynamics of aircraft navigation makes it difficult to estimate the true states. Instead the algorithm studied will estimate the errors of the INS and compensate for them. A height database is used along with the measurements. The height database is highly non-linear why a Rao-Blackwellized particle filter is used for the sensor fusion. This integrated navigation system only uses data from its own sensors and from the height database, which means that it is independent of information from outside the aircraft. This report will describe the algorithm and illustrate the theory used. The main purpose is to evaluate the algorithm using real flight data, why the result chapter is the most important.

<p>One of Saab Space’s products is the S19 guidance system for sounding rockets.Today this system is based on an inertial navigation system that blindly calculatesthe position of the rocket by integrating sensor readings with unknown bias. Thepurpose of this thesis is to integrate a Global Positioning System (GPS) receiverinto the guidance system to increase precision and robustness. There are mainlytwo problems involved in this integration. One is to integrate the GPS with sensorfusion into the existing guidance system. The seconds is to get the GPS satellitetracking to work under extremely high dynamics. The first of the two problems issolved by using an Extended Kalman filter (EKF) with two different linearizations.One of them is uses Euler angles and the other is done with quaternions. Theintegration technique implemented in this thesis is a loose integration between theGPS receiver and the inertial navigation system. The main task of the EKF isto estimate the bias of the inertial navigation system sensors and correct it toeliminate drift in the position. The solution is verified by computing the positionof a car using a GPS and an inertial measurement unit. Different solutions to theGPS tracking problem are proposed in a pre-study.</p> / <p>En av Saab Space produkter är navigationssystemet S19 som styr sondraketer.Fram till idag har systemet varit baserat på ett tröghetsnavigeringssystem somblint räknar ut position genom att integrera tröghetsnavigerinssystemets sensorermed okända biaser. Syftet med detta exjobb är att integrera en GPS med tröghetsnavigeringsystemetför att öka robusthet och precision. Det kan i huvudsak delasupp i två problem; att integrera en GPS-mottagare med det befintliga navigationsystemetmed användning utav sensorfusion, och att få satellitföljningen attfungera under extremt höga dynamiska förhållanden. Det första av de två problemenlöses genom ett Extended Kalman filter (EKF) med två olika linjäriseringar.Den första linjäriseringen är med Eulervinklar och är välbeprövad. Den andra ärmed kvaternioner. Integrationstekniken som implementeras i detta Examensarbeteär en lös integration mellan GPS-mottagaren och tröghetsnavigeringssystemet. Huvudsyftetmed EKF:en är att estimera bias i tröghetsnavigeringsystemets sensoreroch korrigera dem för att eliminera drifter i position. Lösningen verifieras genomatt räkna ut positionen för en bil med GPS och en inertiell mätenhet. Olika lösningartill satellitföljningen föreslås i en förstudie.</p>

Importance of an alternative sensor system to an inertial measurement unit (IMU) is essential for intelligent land navigation systems when the vehicle travels in a GPS deprived environment. The sensor system that has to be used in updating the IMU for a reliable navigation solution has to be a passive sensor system which does not depend on any outside signal. This dissertation presents the results of an effort where position and orientation data from vision and inertial sensors are integrated. Information from a sequence of images captured by a monocular camera attached to a survey vehicle at a maximum frequency of 3 frames per second was used in upgrading the inertial system installed in the same vehicle for its inherent error accumulation. Specifically, the rotations and translations estimated from point correspondences tracked through a sequence of images were used in the integration. However, for such an effort, two types of tasks need to be performed. The first task is the calibration to estimate the intrinsic properties of the vision sensors (cameras), such as the focal length and lens distortion parameters and determination of the transformation between the camera and the inertial systems. Calibration of a two sensor system under indoor conditions does not provide an appropriate and practical transformation for use in outdoor maneuvers due to invariable differences between outdoor and indoor conditions. Also, use of custom calibration objects in outdoor operational conditions is not feasible due to larger field of view that requires relatively large calibration object sizes. Hence calibration becomes one of the critical issues particularly if the integrated system is used in Intelligent Transportation Systems applications. In order to successfully estimate the rotations and translations from vision system the calibration has to be performed prior to the integration process. The second task is the effective fusion of inertial and vision sensor systems. The automated algorithm that identifies point correspondences in images enables its use in real-time autonomous driving maneuvers. In order to verify the accuracy of the established correspondences, independent constraints such as epipolar lines and correspondence flow directions were used. Also a pre-filter was utilized to smoothen out the noise associated with the vision sensor (camera) measurements. A novel approach was used to obtain the geodetic coordinates, i.e. latitude, longitude and altitude, from the normalized translations determined from the vision sensor. Finally, the position locations based on the vision sensor was integrated with those of the inertial system in a decentralized format using a Kalman filter. The vision/inertial integrated position estimates are successfully compared with those from 1) inertial/GPS system output and 2) actual survey performed on the same roadway. This comparison demonstrates that vision can in fact be used successfully to supplement the inertial measurements during potential GPS outages. The derived intrinsic properties and the transformation between individual sensors are also verified during two separate test runs on an actual roadway section.

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

In this thesis, robust set-valued estimation is studied and its application to in-flight alignment of strapdown inertial navigation systems (SINS) with large heading uncertainty is performed. It is known that the performance of the Kalman filter is vulnerable to modeling errors. One of the estimation methods, which are robust against modeling errors, is robust set-valued estimation. In this approach, the filter calculates the set of all possible states, which are consistent with uncertainty inputs satisfying an integral quadratic constraint (IQC) for given measured system outputs. In this thesis, robust set-valued filter with deterministic input is derived. In-flight alignment of SINS with Kalman filtering using external measurements is a widely used technique to eliminate the initial errors. However, if the initial errors are large then the performance of standard Kalman filtering technique is degraded due to modeling error caused by linearization process. To solve this problem, a novel linear norm-bounded uncertain error model is proposed where the remaining second orders terms due to linearization process are considered as norm-bounded uncertainty regarding only the heading error is large. Using the uncertain error model, the robust set-valued filter is applied to in-flight alignment problem. The comparison of the Kalman filter and the robust filter is done on a simulated trajectory and a real-time data. The simulation results show that the modeling errors can be compensated to some extent in Kalman filter by increasing the process noise covariance matrix. However, for very large initial heading errors, the proposed method outperforms the Kalman filter.

Despite significant progress in GPS/INS integration-based direct geo-referencing (DGR) technology over the past decade, its performance still needs to be improved in terms of accuracy and tolerance to GPS outages. This is mainly due to the limited geometric strength of the GPS satellite constellation, the quality of INS and the system integration technology. This research is focused on pseudolite (PL) augmentation to enhance the geometric strength of the GPS satellite constellation, and the Neural Network (NN) aided Kalman filter (KF) system integration algorithm to improve the geo-referencing system's performance during GPS outages. The main research contributions are summarized as below: a) Systematic errors introduced by pseudolites have been investigated. Theoretical and numerical analyses reveal that errors of troposphere delay modelling, differential nonlinearity and pseudolite location are sensitive to pseudolite receiver geometry. Their effect on final positioning solutions can be minimised by selecting optimal pseudolite and receiver locations, which is referred to as geometry design. Optimal geometry design for pseudolite augmented systems has been proposed based on simulation results in airborne surveying scenarios. b) Nonlinear geometry bias, or nonlinearity, exists in single difference processes when the unit vectors from the reference and user receivers to a satellite or pseudolite are non-parallel. Similar to long baseline differential GPS (DGPS), nonlinearity is a serious issue in pseudolite augmentation. A Projected Single Difference (PSD) method has been introduced to eliminate nonlinear geometry bias. An optimized expression has been derived to calculate the direction of project vectors, and the advantages of applying PSD in pseudolite augmented airborne DGPS have been demonstrated. c) A new method for pseudolite tropospheric delay modelling has been proposed, which is based on single-differenced GPS tropospheric delay models. The performance of different models has been investigated through simulations and field testing. The advantages and limitations of each method have been analysed. It is determined that the Bouska model performs relatively well in all ranges and elevations if the meteorological parameters in the models can be accurately collected. d) An adaptive pseudolite tropospheric delay modelling method has been developed to reduce modelling error by estimating meteorological parameters in real-time, using GPS and pseudolite measurements. Test results show that pseudolite tropospheric delay modelling errors can be effectively mitigated by the proposed method. e) A novel geo-referencing system based on GPS/PL/INS integration has been developed as an alternative to existing GPS/INS systems. With the inclusion of pseudolite signals to enhance availability and geometry strength of GPS signals, the continuity and precision of the GPS/INS system can be significantly improved. Flight trials have been conducted to evaluate the system performance for airborne mapping. The results show that the accuracy and reliability of the geo-referenced solution can be improved with the deployment of one or more pseudolites. f) Two KF and NN hybrid methods have been proposed to improve geo-referenced results during GPS outages. As the KF prediction diverges without measurement update, the performance of a GPS/INS integrated system degrades rapidly during GPS outages. Neural networks can overcome this limitation of KF. The first method uses NN to map vehicle manoeuvres with KF measurement in a loosely coupled GPS/INS system. In the second method, an NN is trained to map INS measurements with selected KF error states in a tightly coupled GPS/INS system when GPS signals are available. These training results can be used to modify KF time updates. Optimal input/output and NN structure have been investigated. Field tests show that the proposed hybrid methods can dramatically improve geo-referenced solutions during GPS outages.

Made available in DSpace on 2014-12-17T14:56:18Z (GMT). No. of bitstreams: 1 AndersonBNS_DISSERT.pdf: 1465627 bytes, checksum: c03d80fd5309916ed15fcc6c0a45c8a8 (MD5) Previous issue date: 2013-10-07 / No espa?o tridimensional, um corpo r?gido qualquer pode efetuar transla??es e ou rota??es em rela??o a cada um de seus eixos. Identificar com precis?o o deslocamento realizado por um corpo ? fundamental para alguns tipos de sistemas em engenharia. Em sistemas de navega??o inercial tradicionais, utilizam-se aceler?metros para reconhecer a acelera??o linear e girosc?pios para reconhecer a velocidade angular registrada durante o deslocamento. O girosc?pio, entretanto, ? um dispositivo de custo mais elevado e com alto consumo de energia quando comparado a um aceler?metro. Essa desvantagem deu origem a pesquisas a respeito de sistemas e unidades de medidas inerciais que n?o utilizam girosc?pios. A ideia de utilizar apenas aceler?metros para calcular o movimento linear e angular surgiu no in?cio da d?cada de 60 e vem se desenvolvendo atrav?s de modelos que variam no n?mero de sensores, na maneira como estes s?o organizados e no modelo matem?tico que ? utilizado para derivar o movimento do corpo. Esse trabalho prop?e um esquema de configura??o para constru??o de uma unidade de medida inercial que utiliza tr?s aceler?metros triaxiais. Para identificar o deslocamento de um corpo r?gido a partir deste esquema, foi utilizado um modelo matem?tico que utiliza apenas os nove sinais de acelera??o extra?dos dos tr?s sensores. A proposta sugere que os sensores sejam montados e distribu?dos em formato de L . Essa disposi??o permite a utiliza??o de um ?nico plano do sistema de coordenadas, facilitando assim a instala??o e configura??o destes dispositivos e possibilitando a implanta??o dos sensores em uma ?nica placa de circuito integrado. Os resultados encontrados a partir das simula??es iniciais demonstram a viabilidade da utiliza??o do esquema de configura??o proposto

This master thesis report presents the developement of an INS/GPS navigation loop written in ANSI C++ using a standard matrix library. The filter have been tested on an Unmanned Aerial Vehicle (UAV) called Brumby. Here data have been logged from the Inertial Measurement Unit (IMU) and the Global Positioning System (GPS) receiver. This data have then been postprocessed and run through the navigation filter for estimation of position, attitude and velocity of the vehicle during the flights. The error feedback to the Inertial Navigation System (INS) is done with a complement filter implemented using a kalman filter written in information form.The resulting navigation filter estimates the attitude within two degrees with 95% confidence and the position within two meter using 95% confidence. / <p>Validerat; 20101217 (root)</p>

Within this Master’s thesis, design and construction of a strapdown inertial navigation system based on MEMS sensors is described. The thesis includes theoretic analysis of physics behind determining the position of an object based on the object’s aceleration and changes in the object’s orientation in space. Included is also an overview of mathematical methods related to the position calculation.

QC 20110412