Development and Validation of an IMU/GPS/Galileo Integration Navigation System for UAV

Several and distinct Unmanned Aircraft Vehicle (UAV) applications are emerging, demanding steps to be taken in order to allow those platforms to operate in an un-segregated airspace. The key risk component, hindering the widespread integration of UAV in an un-segregated airspace, is the autonomous component: the need for a high level of autonomy in the UAV that guarantees a safe and secure integration in an un-segregated airspace. At this point, the UAV accurate state estimation plays a fundamental role for autonomous UAV, being one of the main responsibilities of the onboard autopilot. Given the 21st century global economic paradigm, academic projects based on inexpensive UAV platforms but on expensive commercial autopilots start to become a non-economic solution. Consequently, there is a pressing need to overcome this problem through, on one hand, the development of navigation systems using the high availability of low cost, low power consumption, and small size navigation sensors offered in the market, and, on the other hand, using Global Navigation Satellite Systems Software Receivers (GNSS SR). Since the performance that is required for several applications in order to allow UAV to fly in an un-segregated airspace is not yet defined, for most UAV academic applications, the navigation system accuracy required should be at least the same as the one provided by the available commercial autopilots. This research focuses on the investigation of the performance of an integrated navigation system composed by a low performance inertial measurement unit (IMU) and a GNSS SR. A strapdown mechanization algorithm, to transform raw inertial data into navigation solution, was developed, implemented and evaluated. To fuse the data provided by the strapdown algorithm with the one provided by the GNSS SR, an Extended Kalman Filter (EKF) was implemented in loose coupled closed-loop architecture, and then evaluated. Moreover, in order to improve the performance of the IMU raw data, the Allan variance and denoise techniques were considered for both studying the IMU error model and improving inertial sensors raw measurements. In order to carry out the study, a starting question was made and then, based on it, eight questions were derived. These eight secondary questions led to five hypotheses, which have been successfully tested along the thesis. This research provides a deliverable to the Project of Research and Technologies on Unmanned Air Vehicles (PITVANT) Group, consisting of a well-documented UAV Development and Validation of an IMU/GPS/Galileo Integration Navigation System for UAV II navigation algorithm, an implemented and evaluated navigation algorithm in the MatLab environment, and Allan variance and denoising algorithms to improve inertial raw data, enabling its full implementation in the existent Portuguese Air Force Academy (PAFA) UAV. The derivable provided by this thesis is the answer to the main research question, in such a way that it implements a step by step procedure on how the Strapdown IMU (SIMU)/GNSS SR should be developed and implemented in order to replace the commercial autopilot. The developed integrated SIMU/GNSS SR solution evaluated, in post-processing mode, through van-test scenario, using real data signals, at the Galileo Test and Development Environment (GATE) test area in Berchtesgaden, Germany, when confronted with the solution provided by the commercial autopilot, proved to be of better quality. Although no centimetre-level of accuracy was obtained for the position and velocity, the results confirm that the integration strategy outperforms the Piccolo system performance, being this the ultimate goal of this research work

Preliminary design of a redundant strapped down inertial navigation unit using two-degree-of-freedom tuned-gimbal gyroscopes

This redundant strapdown INS preliminary design study demonstrates the practicality of a skewed sensor system configuration by means of: (1) devising a practical system mechanization utilizing proven strapdown instruments, (2) thoroughly analyzing the skewed sensor redundancy management concept to determine optimum geometry, data processing requirements, and realistic reliability estimates, and (3) implementing the redundant computers into a low-cost, maintainable configuration

Quaternionic Attitude Estimation with Inertial Measuring Unit for Robotic and Human Body Motion Tracking using Sequential Monte Carlo Methods with Hyper-Dimensional Spherical Distributions

This dissertation examined the inertial tracking technology for robotics and human tracking applications. This is a multi-discipline research that builds on the embedded system engineering, Bayesian estimation theory, software engineering, directional statistics, and biomedical engineering. A discussion of the orientation tracking representations and fundamentals of attitude estimation are presented briefly to outline the some of the issues in each approach. In addition, a discussion regarding to inertial tracking sensors gives an insight to the basic science and limitations in each of the sensing components. An initial experiment was conducted with existing inertial tracker to study the feasibility of using this technology in human motion tracking. Several areas of improvement were made based on the results and analyses from the experiment. As the performance of the system relies on multiple factors from different disciplines, the only viable solution is to optimize the performance in each area. Hence, a top-down approach was used in developing this system. The implementations of the new generation of hardware system design and firmware structure are presented in this dissertation. The calibration of the system, which is one of the most important factors to minimize the estimation error to the system, is also discussed in details. A practical approach using sequential Monte Carlo method with hyper-dimensional statistical geometry is taken to develop the algorithm for recursive estimation with quaternions. An analysis conducted from a simulation study provides insights to the capability of the new algorithms. An extensive testing and experiments was conducted with robotic manipulator and free hand human motion to demonstrate the improvements with the new generation of inertial tracker and the accuracy and stability of the algorithm. In addition, the tracking unit is used to demonstrate the potential in multiple biomedical applications including kinematics tracking and diagnosis instrumentation. The inertial tracking technologies presented in this dissertation is aimed to use specifically for human motion tracking. The goal is to integrate this technology into the next generation of medical diagnostic system

GPS without satellites

Nowadays, there have been great advances in the location technology. The personal positioning oers a very interesting eld of research because the user walking has an unpredictable behaviour and it is dicult to assume predened routes or to take into account other implemented location techniques for vehicles or robots. An approach for integration between inertial navigation systems (INS) and GPS is presented. GPS is a navigation aid accurate and reliable but susceptible to interference like multipath. An INS is very accurate over short periods, but its errors drift unbounded over time. Blending INS with GPS can remedy the performance issues of both. GPS is often combined with other sensors like accelerometers, gyroscopes or magnetometers. The data fusion from these sensors is very important because they allow us to calculate the position and orientation constantly. In this project we are interested in analysing the system behaviour when the signal GPS is unavailable as when the signal is blocked or in indoor environments. The analysis will be carried out through the assessment of a Dead Reckoning algorithm to improve the position information. The system was tested both indoor and outdoor of the Thales building. The personal positioning system is made up of: a receiver GPS, an electronic compass, and the IMU. There are many types of integration methods, and sensors vary greatly, from the complex and expensive, to the simple and inexpensive, in this project it has been used low cost sensors in a loosely coupled approach. A Kalman alter for closed loop integration between GPS and INS is done. The lter propagates and estimates the error states, which are fed back to the INS for correction of the internal navigation states. The integration algorithm has been implemented on Matlab. The algorithm receives the GPS and inertial measurements via serial port to later process all the data. The algorithm has been used to experimentally test and compare navigation performance

Application of advanced technology to space automation

Automated operations in space provide the key to optimized mission design and data acquisition at minimum cost for the future. The results of this study strongly accentuate this statement and should provide further incentive for immediate development of specific automtion technology as defined herein. Essential automation technology requirements were identified for future programs. The study was undertaken to address the future role of automation in the space program, the potential benefits to be derived, and the technology efforts that should be directed toward obtaining these benefits

Investigation of application of two-degree-of-freedom dry tuned-gimbal gyroscopes to strapdown navigation systems

The work is described which was accomplished during the investigation of the application of dry-tuned gimbal gyroscopes to strapdown navigation systems. A conventional strapdown configuration, employing analog electronics in conjunction with digital attitude and navigation computation, was examined using various levels of redundancy and both orthogonal and nonorthogonal sensor orientations. It is concluded that the cost and reliability performance constraints which had been established could not be met simultaneously with such a system. This conclusion led to the examination of an alternative system configuration which utilizes an essentially new strapdown system concept. This system employs all-digital signal processing in conjunction with the newly-developed large scale integration (LSI) electronic packaging techniques and a new two-degree-of-freedom dry tuned-gimbal instrument which is capable of providing both angular rate and acceleration information. Such a system is capable of exceeding the established performance goals

Space shuttle booster Data Management System (DMS) requirements analysis. Volume 2: Detail requirements

Space shuttle subsystem interface description, subsystem computational requirements, and analysis program - Vol.