International Telemetering Conference Proceedings / October 30-November 02, 1995 / Riviera Hotel, Las Vegas, Nevada / With the development of microcomputer technology, the application of strap-down inertial navigation on aircraft is used more frequently. The attitude measurement for miniature spacecraft is most important. Installing three-axis acceleration sensors and three-axis rate gyros on the spacecraft, the accelerations and attitudes can be obtained through the PCM/FM telemetry system. Then, the initial attitude of spacecraft is given through outside measurement and telemetry. Finally, in the ground station, the parameters of spacecraft attitude are given by using strapdown inertial navigation theory and quanternion differential equation for solving the attitude.

Zugl.: Saarbrücken, Univ., Diss., 2007

A demanda crescente por petróleo impulsiona a exploração marítima desta riqueza para águas cada vez mais profundas. O aumento da lâmina dágua exige novas soluções de engenharia principalmente no que se refere à operação de unidades flutuantes de produção. Dentre os desafios impostos pelos novos ambientes de prospecção, destaca-se o processo de ancoragem, neste texto explorado sob a ótica da chamada estaca-torpedo. Embora já tenha sido empregada com sucesso na ancoragem de risers e FPSOs, esta solução encontra-se em constante desenvolvimento, sendo a principal fonte de informação acerca dos lançamentos da estaca-torpedo proveniente de uma unidade de medição inercial (UMI). A presente pesquisa baseou-se no estudo desta UMI e teve como objetivos principais verificar seu desempenho e compreender a empregabilidade deste tipo de monitoração em operações submarinas de maneira mais ampla. Além do estudo detalhado dos sensores, foi dada especial atenção aos algoritmos empregados no tratamento dos sinais provenientes da UMI. Foram estudadas técnicas de correção do sinal, quantificação de ruído, desafios inerentes aos sistemas do tipo strapdown e o processo de integração. Como resultado final foi desenvolvido um algoritmo baseado em quatérnios, alternativo ao atualmente empregado para o processamento dos sinais provenientes da UMI que equipa a estaca-torpedo. / The increasing demand on crude oils constantly pushes the offshore exploitation to deeper waters. As the water depth grows, new engineering challenges arise, especially concerning to the operation of floating production units. Among all the technical issues inherent to the new prospection environment, the mooring system is a significant topic and the development of the torpedo-pile takes place at this scenario. This mooring system has already been successfully applied in anchoring risers and FPSOs; nevertheless, it is in constant study and improvement. The major source of information about the torpedo-pile deployment comes from an inertial measurement unit (IMU). The research presented here is based on this IMU and had as main objective verify its performance and also comprehend the applicability of such kind of unit in other subsea processes. Along with the detailed sensors study, it was given special attention to the algorithms used to process the signals from the IMU. Signal correction techniques and noise quantification were investigated as long as challenges intrinsically related to strapdown navigation systems and the integration process. In the end, an alternative data processing algorithm based on quaternions was produced, to be employed in torpedo-pile launching together with its IMU.

A demanda crescente por petróleo impulsiona a exploração marítima desta riqueza para águas cada vez mais profundas. O aumento da lâmina dágua exige novas soluções de engenharia principalmente no que se refere à operação de unidades flutuantes de produção. Dentre os desafios impostos pelos novos ambientes de prospecção, destaca-se o processo de ancoragem, neste texto explorado sob a ótica da chamada estaca-torpedo. Embora já tenha sido empregada com sucesso na ancoragem de risers e FPSOs, esta solução encontra-se em constante desenvolvimento, sendo a principal fonte de informação acerca dos lançamentos da estaca-torpedo proveniente de uma unidade de medição inercial (UMI). A presente pesquisa baseou-se no estudo desta UMI e teve como objetivos principais verificar seu desempenho e compreender a empregabilidade deste tipo de monitoração em operações submarinas de maneira mais ampla. Além do estudo detalhado dos sensores, foi dada especial atenção aos algoritmos empregados no tratamento dos sinais provenientes da UMI. Foram estudadas técnicas de correção do sinal, quantificação de ruído, desafios inerentes aos sistemas do tipo strapdown e o processo de integração. Como resultado final foi desenvolvido um algoritmo baseado em quatérnios, alternativo ao atualmente empregado para o processamento dos sinais provenientes da UMI que equipa a estaca-torpedo. / The increasing demand on crude oils constantly pushes the offshore exploitation to deeper waters. As the water depth grows, new engineering challenges arise, especially concerning to the operation of floating production units. Among all the technical issues inherent to the new prospection environment, the mooring system is a significant topic and the development of the torpedo-pile takes place at this scenario. This mooring system has already been successfully applied in anchoring risers and FPSOs; nevertheless, it is in constant study and improvement. The major source of information about the torpedo-pile deployment comes from an inertial measurement unit (IMU). The research presented here is based on this IMU and had as main objective verify its performance and also comprehend the applicability of such kind of unit in other subsea processes. Along with the detailed sensors study, it was given special attention to the algorithms used to process the signals from the IMU. Signal correction techniques and noise quantification were investigated as long as challenges intrinsically related to strapdown navigation systems and the integration process. In the end, an alternative data processing algorithm based on quaternions was produced, to be employed in torpedo-pile launching together with its IMU.

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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This research concerns the development of a smart sensory system for tracking a hand-held moving device to millimeter accuracy, for slow or nearly static applications over extended periods of time. Since different operators in different applications may use the system, the proposed design should provide the accurate position, orientation, and velocity of the object without relying on the knowledge of its operation and environment, and based purely on the motion that the object experiences. This thesis proposes the design of the integration a low-cost Local Positioning System (LPS) and a low-cost StrapDown Inertial Navigation System (SDINS) with the association of the modified EKF to determine 3D position and 3D orientation of a hand-held tool within a required accuracy. A hybrid LPS/SDINS combines and complements the best features of two different navigation systems, providing a unique solution to track and localize a moving object more precisely. SDINS provides continuous estimates of all components of a motion, but SDINS loses its accuracy over time because of inertial sensors drift and inherent noise. LPS has the advantage that it can possibly get absolute position and velocity independent of operation time; however, it is not highly robust, is computationally quite expensive, and exhibits low measurement rate. This research consists of three major parts: developing a multi-camera vision system as a reliable and cost-effective LPS, developing a SDINS for a hand-held tool, and developing a Kalman filter for sensor fusion. Developing the multi-camera vision system includes mounting the cameras around the workspace, calibrating the cameras, capturing images, applying image processing algorithms and features extraction for every single frame from each camera, and estimating the 3D position from 2D images. In this research, the specific configuration for setting up the multi-camera vision system is proposed to reduce the loss of line of sight as much as possible. The number of cameras, the position of the cameras with respect to each other, and the position and the orientation of the cameras with respect to the center of the world coordinate system are the crucial characteristics in this configuration. The proposed multi-camera vision system is implemented by employing four CCD cameras which are fixed in the navigation frame and their lenses placed on semicircle. All cameras are connected to a PC through the frame grabber, which includes four parallel video channels and is able to capture images from four cameras simultaneously. As a result of this arrangement, a wide circular field of view is initiated with less loss of line-of-sight. However, the calibration is more difficult than a monocular or stereo vision system. The calibration of the multi-camera vision system includes the precise camera modeling, single camera calibration for each camera, stereo camera calibration for each two neighboring cameras, defining a unique world coordinate system, and finding the transformation from each camera frame to the world coordinate system. Aside from the calibration procedure, digital image processing is required to be applied into the images captured by all four cameras in order to localize the tool tip. In this research, the digital image processing includes image enhancement, edge detection, boundary detection, and morphologic operations. After detecting the tool tip in each image captured by each camera, triangulation procedure and optimization algorithm are applied in order to find its 3D position with respect to the known navigation frame. In the SDINS, inertial sensors are mounted rigidly and directly to the body of the tracking object and the inertial measurements are transformed computationally to the known navigation frame. Usually, three gyros and three accelerometers, or a three-axis gyro and a three-axis accelerometer are used for implementing SDINS. The inertial sensors are typically integrated in an inertial measurement unit (IMU). IMUs commonly suffer from bias drift, scale-factor error owing to non-linearity and temperature changes, and misalignment as a result of minor manufacturing defects. Since all these errors lead to SDINS drift in position and orientation, a precise calibration procedure is required to compensate for these errors. The precision of the SDINS depends not only on the accuracy of calibration parameters but also on the common motion-dependent errors. The common motion-dependent errors refer to the errors caused by vibration, coning motion, sculling, and rotational motion. Since inertial sensors provide the full range of heading changes, turn rates, and applied forces that the object is experiencing along its movement, accurate 3D kinematics equations are developed to compensate for the common motion-dependent errors. Therefore, finding the complete knowledge of the motion and orientation of the tool tip requires significant computational complexity and challenges relating to resolution of specific forces, attitude computation, gravity compensation, and corrections for common motion-dependent errors. The Kalman filter technique is a powerful method for improving the output estimation and reducing the effect of the sensor drift. In this research, the modified EKF is proposed to reduce the error of position estimation. The proposed multi-camera vision system data with cooperation of the modified EKF assists the SDINS to deal with the drift problem. This configuration guarantees the real-time position and orientation tracking of the instrument. As a result of the proposed Kalman filter, the effect of the gravitational force in the state-space model will be removed and the error which results from inaccurate gravitational force is eliminated. In addition, the resulting position is smooth and ripple-free. The experimental results of the hybrid vision/SDINS design show that the position error of the tool tip in all directions is about one millimeter RMS. If the sampling rate of the vision system decreases from 20 fps to 5 fps, the errors are still acceptable for many applications.

This research concerns the development of a smart sensory system for tracking a hand-held moving device to millimeter accuracy, for slow or nearly static applications over extended periods of time. Since different operators in different applications may use the system, the proposed design should provide the accurate position, orientation, and velocity of the object without relying on the knowledge of its operation and environment, and based purely on the motion that the object experiences. This thesis proposes the design of the integration a low-cost Local Positioning System (LPS) and a low-cost StrapDown Inertial Navigation System (SDINS) with the association of the modified EKF to determine 3D position and 3D orientation of a hand-held tool within a required accuracy. A hybrid LPS/SDINS combines and complements the best features of two different navigation systems, providing a unique solution to track and localize a moving object more precisely. SDINS provides continuous estimates of all components of a motion, but SDINS loses its accuracy over time because of inertial sensors drift and inherent noise. LPS has the advantage that it can possibly get absolute position and velocity independent of operation time; however, it is not highly robust, is computationally quite expensive, and exhibits low measurement rate. This research consists of three major parts: developing a multi-camera vision system as a reliable and cost-effective LPS, developing a SDINS for a hand-held tool, and developing a Kalman filter for sensor fusion. Developing the multi-camera vision system includes mounting the cameras around the workspace, calibrating the cameras, capturing images, applying image processing algorithms and features extraction for every single frame from each camera, and estimating the 3D position from 2D images. In this research, the specific configuration for setting up the multi-camera vision system is proposed to reduce the loss of line of sight as much as possible. The number of cameras, the position of the cameras with respect to each other, and the position and the orientation of the cameras with respect to the center of the world coordinate system are the crucial characteristics in this configuration. The proposed multi-camera vision system is implemented by employing four CCD cameras which are fixed in the navigation frame and their lenses placed on semicircle. All cameras are connected to a PC through the frame grabber, which includes four parallel video channels and is able to capture images from four cameras simultaneously. As a result of this arrangement, a wide circular field of view is initiated with less loss of line-of-sight. However, the calibration is more difficult than a monocular or stereo vision system. The calibration of the multi-camera vision system includes the precise camera modeling, single camera calibration for each camera, stereo camera calibration for each two neighboring cameras, defining a unique world coordinate system, and finding the transformation from each camera frame to the world coordinate system. Aside from the calibration procedure, digital image processing is required to be applied into the images captured by all four cameras in order to localize the tool tip. In this research, the digital image processing includes image enhancement, edge detection, boundary detection, and morphologic operations. After detecting the tool tip in each image captured by each camera, triangulation procedure and optimization algorithm are applied in order to find its 3D position with respect to the known navigation frame. In the SDINS, inertial sensors are mounted rigidly and directly to the body of the tracking object and the inertial measurements are transformed computationally to the known navigation frame. Usually, three gyros and three accelerometers, or a three-axis gyro and a three-axis accelerometer are used for implementing SDINS. The inertial sensors are typically integrated in an inertial measurement unit (IMU). IMUs commonly suffer from bias drift, scale-factor error owing to non-linearity and temperature changes, and misalignment as a result of minor manufacturing defects. Since all these errors lead to SDINS drift in position and orientation, a precise calibration procedure is required to compensate for these errors. The precision of the SDINS depends not only on the accuracy of calibration parameters but also on the common motion-dependent errors. The common motion-dependent errors refer to the errors caused by vibration, coning motion, sculling, and rotational motion. Since inertial sensors provide the full range of heading changes, turn rates, and applied forces that the object is experiencing along its movement, accurate 3D kinematics equations are developed to compensate for the common motion-dependent errors. Therefore, finding the complete knowledge of the motion and orientation of the tool tip requires significant computational complexity and challenges relating to resolution of specific forces, attitude computation, gravity compensation, and corrections for common motion-dependent errors. The Kalman filter technique is a powerful method for improving the output estimation and reducing the effect of the sensor drift. In this research, the modified EKF is proposed to reduce the error of position estimation. The proposed multi-camera vision system data with cooperation of the modified EKF assists the SDINS to deal with the drift problem. This configuration guarantees the real-time position and orientation tracking of the instrument. As a result of the proposed Kalman filter, the effect of the gravitational force in the state-space model will be removed and the error which results from inaccurate gravitational force is eliminated. In addition, the resulting position is smooth and ripple-free. The experimental results of the hybrid vision/SDINS design show that the position error of the tool tip in all directions is about one millimeter RMS. If the sampling rate of the vision system decreases from 20 fps to 5 fps, the errors are still acceptable for many applications.