This research analyzes and demonstrates the local position determination problem on Earth using a novel instrument, the Compass Star Tracker. Special focus is given to the theoretical development of the mathematics of local position determination, the design and fabrication of a proof-of-concept instrument, an error source analysis, and the experimental tests used to validate the position determination concepts. Star sensors are typically used as attitude determination instruments on spacecraft orbiting Earth. In this capacity, the star sensor determines the orientation of the spacecraft using digital images of the stars. This research utilizes the basic functionality of the star sensor in a new way; the orientation information from the star image is used to determine a user's latitude and longitude coordinates on Earth. This concept is valuable because it allows users to determine their position autonomously. The fundamental concepts that enable local position determination were originally published in Drs. Samaan, Mortari, and Junkins (AAS 04-007). This research improves upon that work by eliminating the zenith-orientation constraint and providing several crucial theoretical corrections. In addition to the position determination mathematics, this research provides analysis of the theoretical and practical error sources associated with the position determination problem. This research also details the design, fabrication, and experimental test program of a proof-of-concept Compass Star Tracker. Together, the theoretical development, error analysis, instrument design, and test program serve as validation of the the position determination concept. This work is intended as the first of many steps toward eventual deployment of autonomous position determination sensors on the Moon and Mars.

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 dissertation deals with attitude estimation of multicopters. Mainly the use of multicopter dynamic model in order to gain accuracy is investigated. It shows that the usage of multicopter dynamic model brings advantage contrary to other known algorithms for attitude estimation such as GPS/INS or complementary filter. Besides others one goal is to study the possibilities to estimate the parameters of dynamic model on-line. Further the influence of wind speed to estimation accuracy is also investigated. The algorithms are based on a nonlinear Kalman filter. The use of dynamic model of multicopters reveals the possibility of estimating attitude with bounded error even without periodic measurement of absolute position. One of the results of the dissertation is a new algorithm which does not require information about the thrust of multicopter propellers.

Automatic detection of space objects in optical images is important to close proximity operations, relative navigation, and situational awareness. To better protect space assets, it is very important not only to know where a space object is, but also what the object is. In this dissertation, a method for detecting multiple 1U, 2U, 3U, and 6U CubeSats based on the faster region-based convolutional neural network (Faster R-CNN) is described. CubeSats detection models are developed using Web-searched and computer-aided design images. In addition, a two-step method is presented for detecting a rotating CubeSat in close proximity from a sequence of images without the use of intrinsic or external camera parameters. First, a Faster R-CNN trained on synthetic images of 1U, 2U, 3U, and 6U CubeSats locates the CubeSat in each image and assigns a weight to each CubeSat class. Then, these classification results are combined using Dempster's rule. The method is tested on simulated scenarios where the rotating 3U and 6U CubeSats are in unfavorable views or in dark environments. Faster R-CNN detection results contain useful information for tracking, navigation, pose estimation, and simultaneous localization and mapping. A coarse single-point attitude estimation method is proposed utilizing the centroids of the bounding boxes surrounding the CubeSats in the image. The centroids define the line-of-sight (LOS) vectors to the detected CubeSats in the camera frame, and the LOS vectors in the reference frame are assumed to be obtained from global positioning system (GPS). The three-axis attitude is determined from the vector observations by solving Wahba's problem. The attitude estimation concept is tested on simulated scenarios using Autodesk Maya.

Studies aimed at the development of indigenous low cost star tracker and gyroless attitude and angular rate estimation is presented in the thesis. This study is required for the realization of low cost micro satellites. A target specification of determining the attitude with accuracy (3σ) of 0.05 degrees and attitude rate with accuracy (3σ) in the range of 50rad/sec at a rate of 10 samples/second in all the axes is set as a goal for the study. Different sensor arrays available in the market are evaluated on the basis of their noise characteristics, resolution of the analog-to-digital converter (ADC) present and ability to work in low light conditions, for possible use in the hardware realization of star tracker. STAR1000 APS CMOS array, manufactured by Cypress Semiconductors, qualified these performance criteria, is used for the simulation study. An algorithm is presented for scanning the sensor array, detection of star image and retrieving the information concerning the photoelectron counts corresponding to a star image. The exact designation of the center of the star image becomes crucial as it has direct implications on the accuracy of the estimated attitude. Various algorithms concerning the centroid estimation of a defocused star image on the sensor array to subpixel accuracy are studied and Gaussian Weighed Center of Gravity algorithm is adapted with some modifications and an accuracy of 0.039 pixels is obtained in both horizontal and vertical direction of the array. A one-to-one relationship is established between the stars obtained in the field-of-view (FOV) of the star tracker with the stars present in the star catalog resident in the star tracker through star identification algorithm. A star identification algorithm which relies on the interstar angles and brightness of the stars is developed in this thesis. The interstar angles of the stars visible in the FOV of the star sensor is recorded, compared with the inter-star angles made by the stars selected in the catalog, based on initial brightness match with stars formed on image plane. After identification at the initial epoch, consequent instants can drive information from the previous matches so as to decrease the computational complexity and storage requirement for the subsequent instants. The memory constraints and computational overhead on the processor and the dynamic range of the image detector used in the star tracker are the limiting factors. The stars thus identified with the stars in the catalog are used for the estimation of attitude. A point solution to the attitude estimation problem is computed using a least square based algorithm called ESOQ-2. The algorithm for centroiding of star images and ESOQ-2 for finding the point solution satellite attitude is coded and tested on Da Vinci based emulator. This exercise shows that it is possible to implement above algorithm for real time operations. Estimation of attitude at a given epoch using algorithms like ESOQ-2 does not minimize the noise and error covariance as the attitude estimated at each instant of time depends only on the measurement taken at that particular instant. So a Kalman Filter (KF) based estimation using Integrated Rate Parameter (IRP) formulation called SIAVE algorithm, is adapted, with some modifications, for the estimation of incremental angle and attitude rates from vector observations of stars. From the point solution of attitude estimation problem of the satellite, the incremental angle and angular rate at successive time steps are predicted using a linear KF and refined with the measurements from the stand alone star tracker, taken at discrete time steps, using the SIAVE algorithm. The sensor noise is modeled from the characteristics of STAR1000 sensor array used in the algorithm in order to make the simulations more realistic in nature. The optimality of Kalman filter is based on the assumption that the state and measurement noises are white gaussian random processes and the state dynamics of the plant is completely known. However, for most real systems, modeling uncertainties are present. So a robust state estimator based on H∞ norm minimization is devised. The H∞ filter, based on game theory approach is used to minimize the worst case variance of noise signals with the only assumption on the noise signals that they are energy bounded. The aim is to find the feasibility of using H∞ filter for the estimation of incremental angle and attitude rate of the satellite. The studies shows that H∞ filter with proper tuning can serve as potential estimation scheme for the attitude and angular rate estimation of the satellite. It is found that both Kalman filter and H∞ are able to meet the specified accuracy desired from low cost accurate star sensor.

This dissertation addresses the problem of rigid-body attitude tracking control under three scenarios of high relevance to many aerospace guidance and control applications: adaptive attitude-tracking control law development for a spacecraft with time-varying inertia parameters, velocity-free attitude stabilization using only vector measurements for feedback, and smooth angular velocity observer design for attitude tracking in the absence of angular velocity measurements. Inertia matrix changes in spacecraft applications often occur due to fuel depletion or mass displacement in a flexible or deployable spacecraft. As such, an adaptive attitude control algorithm that delivers consistent performance when faced with uncertain time-varying inertia parameters is of significant interest. This dissertation presents a novel adaptive control algorithm that directly compensates for inertia variations that occur as either pure functions of the control input, or as functions of time and/or the state. Another important problem considered in this dissertation pertains to rigid-body attitude stabilization of a spacecraft when only a set of inertial sensor measurements are available for feedback. A novel gyro-free attitude stabilization solution is presented that directly utilizes unit vector measurements obtained from inertial sensors without relying on observers to reconstruct the spacecraft's attitude or angular velocity. As the third major contribution of this dissertation, the problem of attitude tracking control in the absence of angular velocity measurements is investigated through angular velocity observer (estimator) design. A new angular velocity observer is presented which is smoothed and ensures asymptotic convergence of the estimation errors irrespective of the initial true states of the spacecraft. The combined implementation of a separately designed proportional-derivative type controller using estimates generated by the observer results in global asymptotic stability of the overall closed-loop tracking error dynamics. Accordingly, a separation-type property is established for the rigid-body attitude dynamics, the first such result to the author's best knowledge, using a smooth (switching-free) observer formulation. / text

International Telemetering Conference Proceedings / October 30-November 02, 1995 / Riviera Hotel, Las Vegas, Nevada / The Stanford University Satellite Systems Development Laboratory will flight test a telemetry reengineering experiment on its student-built SAPPHIRE spacecraft. This experiment utilizes solar panel current information and knowledge of panel geometry in order to create a virtual sun sensor that can roughly determine the satellite's sun angle. The Omni-Directional Differential Sun Sensor (ODDSS) algorithm normalizes solar panel currents and differences them to create a quasi-linear signal over a particular sensing region. The specific configuration of the SAPPHIRE spacecraft permits the construction of 24 such regions. The algorithm will account for variations in panel outputs due to battery charging, seasonal fluctuations, solar cell degradation, and albedo affects. Operationally, ODDSS telemetry data will be verified through ground processing and comparison with data derived from SAPPHIRE's infrared sensors and digital camera. The expected sensing accuracy is seven degrees. This paper reviews current progress in the design and integration of the ODDSS algorithm through a discussion of the algorithm's strategy and a presentation of results from hardware testing and software simulation.

This thesis proposes a motion sensing system for wheelchairs with the main objective of determining tips, falls and risky situations. The system relies on measurements from an Inertial Measurement Unit, (IMU), consisting of a 3-axis accelerometer and a 2-axis gyroscope as the source of information. The IMU was embedded in a portable device, the "Motion Logger", which collects motion data in a Secure Digital memory card after running a real time preprocessing algorithm. The algorithm was designed to reduce energy consumption and memory usage. Actual signal analysis and attitude estimation is carried out offline. The motion sensing system was developed for determining wheelchair-related falls as part of a major research effort carried out at the research center of the James A Haley VA Hospital Subject Safety Center, Tampa, Florida. The focus of the study concentrated on achieving a thorough understanding of the demographics, nature, consequences and the creation of prediction models for fall events. The main goal of the embedded system was to successfully estimate the motion variables relevant to the occurrence of falls, tips and similar risky situations. Currently, off-line smoothing techniques based on Kalman filter concepts allow for optimal estimation of angles in the longitudinal direction, roll, and in the lateral direction, pitch. Results from both predefined experiments with known outcomes and data collected from actual wheelchair users during pilot and final deployment stages are presented and discussed.

<p>The quality of cameras is currently increasing very fast meanwhile the price of them is decreasing. The possibilities of using a camera as a measurement and navigation instrument are thus getting bigger all the time. This thesis studies the transformation relations between a camera image and the scene in space that is projected to it. A theoretical derivation of the transform will be presented, and methods and algorithms for applications based on the transform will be developed.</p><p>The above mentioned transform is called the camera matrix, which contains information about the camera attitude, the camera position, and the internal structure of the camera. Useful information for several different applications can be extracted from the camera image with the help of the camera matrix.</p><p>In one of the applications, treated in this Master´s thesis, the camera attitude is estimated when the camera is calibrated and its position is known. Another application is that of absolute target positioning, where a point in a digital map is searched from its position in a camera image. Better accuracy in the measurements can though be obtained with relative target positioning i.e., estimation of distance and angle between two points in the digital map by picking them out in the image. This is because that the errors of the</p><p>absolute target positioning for each of the two points are dependent and thus partly will cancel each other out when their relative position and angle is measured.</p>

The quality of cameras is currently increasing very fast meanwhile the price of them is decreasing. The possibilities of using a camera as a measurement and navigation instrument are thus getting bigger all the time. This thesis studies the transformation relations between a camera image and the scene in space that is projected to it. A theoretical derivation of the transform will be presented, and methods and algorithms for applications based on the transform will be developed. The above mentioned transform is called the camera matrix, which contains information about the camera attitude, the camera position, and the internal structure of the camera. Useful information for several different applications can be extracted from the camera image with the help of the camera matrix. In one of the applications, treated in this Master´s thesis, the camera attitude is estimated when the camera is calibrated and its position is known. Another application is that of absolute target positioning, where a point in a digital map is searched from its position in a camera image. Better accuracy in the measurements can though be obtained with relative target positioning i.e., estimation of distance and angle between two points in the digital map by picking them out in the image. This is because that the errors of the absolute target positioning for each of the two points are dependent and thus partly will cancel each other out when their relative position and angle is measured.