An autonomous, real-time orbit determination system was developed within thiswork for the next iteration of the University of W¨urzburg’s CubeSat programme.The algorithm only made use of magnetometer and sun sensors, which already wereimplemented on UWE-3, the third satellite in the programme. Previous developedsystems used the same approach, however the unique aspect in this work is thatthe algorithm was implemented using equinoctial elements.A Runge-Kutta-4 integrator propagated the orbit position using the orbit dynamicsunder the consideration of J2-perturbations. Afterwards, an Extended KalmanFilter corrected the position through processing the two measurements.The algorithm was then tested under multiple conditions. At first, a two weekstability test was conducted using simulated data, followed by a test with recordedsatellite data. These have shown a mean error of 13.2 km and 12.6 km respectively.Lastly, the algorithm was translated in to C and evaluated on a micro-controller.

"This work addresses the problem of generating a 3D mesh grid model of a tree by a climbing robot for tree inspection. In order to generate a consistent model of the tree while climbing, the robot needs to be able to track its location while generating the model. Hence we explored this problem as a subset of Simultaneous Localization and Mapping problem. The monocular camera based Visual Simultaneous Localization and Mapping(VSLAM) algorithm was adopted to map the features on the tree. Multi-scale grid based FAST feature detector combined with Lucas Kande Optical flow was used to extract features from the tree. Inverse depth representation of feature was selected to seamlessly handle newly initialized features. The camera and the feature states along with their co-variances are managed in an Extended Kalman filter. In our VSLAM implementation we have attempted to track a large number of features. From the sparse spatial distribution of features we get using Extended Kalman filter we attempt to generate a 3D mesh grid model with the help of an unordered triangle fitting algorithm. We explored the implementation in C++ using Eigen, OpenCV and Point Cloud Library. A multi-threaded software design of the VSLAM algorithm was implemented. The algorithm was evaluated with image sets from trees susceptible to Asian Long Horn Beetle. "

This thesis demonstrates that any individual can provide relevant observational data to further research efforts within the Aerospace community, through the use of amateur telescopes. A Meade LX200 12 in. telescope and Lumenera Skynyx 2.0 camera were utilized to observe small LEO satellites, using a well-documented point-and-wait staring method. Over a period of three months, a total of 186 observation attempts were made resulting in 97 successful captures. From the gathered data, three possible aerospace applications were analyzed: validation of a satellite brightness prediction model, angles-only orbit determination including extended Kalman filtering, and temporal error growth in TLE-based orbit propagation. Further investigations include a preliminary optimization using MATLAB's fmincon function (informed by the previous analyses) to determine an optimal telescope size for performing LEO observations.

<p>This thesis aims the problem of underwater vehicle positioning. The vehicle usedwas a Saab Seaeye Falcon which was equipped with a Doppler Velocity Log(DVL)manufactured by RD Instruments and an inertial measurement unit (IMU) fromXsense. During the work several different Extended Kalman Filter (EKF) havebeen tested both with a hydrodynamic model of the vehicle and a model withconstant acceleration and constant angular velocity. The filters were tested withdata from test runs in lake Vättern. The EKF with constant acceleration andconstant angular velocity appeared to be the better one. The misalignment of thesensors were also tried to be estimated but with poor result.</p>

This dissertation presents an experimental and analytical study of the Vision Based Navigation system (VisNav). VisNav is a novel intelligent optical sensor system invented by Texas A&M University recently for autonomous proximity operations. This dissertation is focused on system calibration techniques and navigation algorithms. This dissertation is composed of four parts. First, the fundamental hardware and software design configuration of the VisNav system is introduced. Second, system calibration techniques are discussed that should enable an accurate VisNav system application, as well as characterization of errors. Third, a new six degree-of-freedom navigation algorithm based on the Gaussian Least Squares Differential Correction is presented that provides a geometrical best position and attitude estimates through batch iterations. Finally, a dynamic state estimation algorithm utilizing the Extended Kalman Filter (EKF) is developed that recursively estimates position, attitude, linear velocities, and angular rates. Moreover, an approach for integration of VisNav measurements with those made by an Inertial Measuring Unit (IMU) is derived. This novel VisNav/IMU integration technique is shown to significantly improve the navigation accuracy and guarantee the robustness of the navigation system in the event of occasional dropout of VisNav data.

A new algorithm is proposed that smoothly integrates the nonlinear estimation of the attitude quaternion using Davenport's q-method and the estimation of non-attitude states within the framework of an extended Kalman filter. A modification to the q-method and associated covariance analysis is derived with the inclusion of an a priori attitude estimate. The non-attitude states are updated from the nonlinear attitude estimate based on linear optimal Kalman filter techniques. The proposed filter is compared to existing methods and is shown to be equivalent to second-order in the attitude update and exactly equivalent in the non-attitude state update with the Sequential Optimal Attitude Recursion filter. Monte Carlo analysis is used in numerical simulations to demonstrate the validity of the proposed approach. This filter successfully estimates the nonlinear attitude and non-attitude states in a single Kalman filter without the need for iterations.

This thesis aims the problem of underwater vehicle positioning. The vehicle usedwas a Saab Seaeye Falcon which was equipped with a Doppler Velocity Log(DVL)manufactured by RD Instruments and an inertial measurement unit (IMU) fromXsense. During the work several different Extended Kalman Filter (EKF) havebeen tested both with a hydrodynamic model of the vehicle and a model withconstant acceleration and constant angular velocity. The filters were tested withdata from test runs in lake Vättern. The EKF with constant acceleration andconstant angular velocity appeared to be the better one. The misalignment of thesensors were also tried to be estimated but with poor result.

This thesis presents mobile positioning and tracking with non-line of sight (NLOS) mitigation using time difference of arrival (TDOA) in biased extended Kalman filter (BEKF) in indoor dense multipath Ultra-Wideband (UWB) environment. The most serious issues which render to influence accuracy for the time-based location system is NLOS problem. Kalman filters (KFs) are used for smoothing range measurement data, and a method with sliding window is proposed to process range data for calculating standard deviation in a hypothesis testing and then identifying NLOS scenarios. When the measured arrival time has been converted to range difference, the biased extended Kalman filter is proposed to mitigate the NLOS error in the certain base stations (BSs) for mobile station (MS) positioning and trajectory tracking. From the simulation results in the indoor positioning environment with measurement and NLOS error, the sliding window algorithm and biased extended Kalman filter have higher accuracy than other related methods for NLOS identification and mitigation in positioning.

This dissertation presents an experimental and analytical study of the Vision Based Navigation system (VisNav). VisNav is a novel intelligent optical sensor system invented by Texas A&M University recently for autonomous proximity operations. This dissertation is focused on system calibration techniques and navigation algorithms. This dissertation is composed of four parts. First, the fundamental hardware and software design configuration of the VisNav system is introduced. Second, system calibration techniques are discussed that should enable an accurate VisNav system application, as well as characterization of errors. Third, a new six degree-of-freedom navigation algorithm based on the Gaussian Least Squares Differential Correction is presented that provides a geometrical best position and attitude estimates through batch iterations. Finally, a dynamic state estimation algorithm utilizing the Extended Kalman Filter (EKF) is developed that recursively estimates position, attitude, linear velocities, and angular rates. Moreover, an approach for integration of VisNav measurements with those made by an Inertial Measuring Unit (IMU) is derived. This novel VisNav/IMU integration technique is shown to significantly improve the navigation accuracy and guarantee the robustness of the navigation system in the event of occasional dropout of VisNav data.

The use of ubiquitous network has made real time tracking of objects, animals and human beings easy through the use of radio frequency identification system (RFID). Localization techniques in RFID rely on accurate estimation of the read range between the reader and the tags. The tags consist of a small chip and a printed antenna which receives from and transmits information to the reader. The range information about the distance between the tag and the reader is obtained from the received signal strength indication (RSSI). Accuracy of the read range using RSSI can be very complicated especially in complicated propagation environment due to the nature and features of the environment. There are different kinds of localisation systems and they are Global Positioning System (GPS) which can be used for accurate outdoor localization; while technologies like artificial vision, ultrasonic signals, infrared and radio frequency signals can be employed for indoor localization. This project focuses on the location estimation in RFID Non Line-of-Sight (NLOS) environment using Real Time Localization System (RTLS) with passive tags, in carrying out passengers and baggage tracking at the airport. Indoor location radio sensing suffers from reflection, refraction and diffractions due to the nature of the environment. This unfavourable phenomenon called multipath leads to delay in the arrival of signal and the strength of signal received by receiving antenna within the propagation channel which in turns affects the RSSI, yielding inaccurate location estimation. RTLS based on time difference of arrival and error compensation technique and extended Kalman filter technique were employed in a NLOS environment to determine the location of tag. The better method for location estimation in a NLOS between the Kalman filtering and extended Kalman filtering is investigated. According to simulation results, the extended Kalman filtering technique is more suitable to be applied to RTLS.