Vision-Aided Autonomous Precision Weapon Terminal Guidance Using a Tightly-Coupled INS and Predictive Rendering Techniques

This thesis documents the development of the Vision-Aided Navigation using Statistical Predictive Rendering (VANSPR) algorithm which seeks to enhance the endgame navigation solution possible by inertial measurements alone. The eventual goal is a precision weapon that does not rely on GPS, functions autonomously, thrives in complex 3-D environments, and is impervious to jamming. The predictive rendering is performed by viewpoint manipulation of computer-generated of target objects. A navigation solution is determined by an Unscented Kalman Filter (UKF) which corrects positional errors by comparing camera images with a collection of statistically significant virtual images. Results indicate that the test algorithm is a viable method of aiding an inertial-only navigation system to achieve the precision necessary for most tactical strikes. On 14 flight test runs, the average positional error was 166 feet at endgame, compared with an inertial-only error of 411 feet

Leveraging External Sensor Data for Enhanced Space Situational Awareness

Reliable Space Situational Awareness (SSA) is a recognized requirement in the current congested, contested, and competitive environment of space operations. A shortage of available sensors and reliable data sources are some current limiting factors for maintaining SSA. Unfortunately, cost constraints prohibit drastically increasing the sensor inventory. Alternative methods are sought to enhance current SSA, including utilizing non-traditional data sources (external sensors) to perform basic SSA catalog maintenance functions. Astronomical data, for example, routinely collects serendipitous satellite streaks in the course of observing deep space; but tactics, techniques, and procedures designed to glean useful information from those collects have yet to be rigorously developed. This work examines the feasibility and utility of performing ephemeris positional updates for a Resident Space Object (RSO) catalog using metric data obtained from RSO streaks gathered by astronomical telescopes. The focus of this work is on processing data from three possible streak categories: streaks that only enter, only exit, or cross completely through the astronomical image. Successful use of this data will aid in resolving uncorrelated tracks, space object identification, and threat detection. Incorporation of external data sources will also reduce the number of routine collects required by existing SSA sensors, freeing them up for more demanding tasks. The results clearly demonstrate that accurate orbital reconstruction can be performed using an RSO streak in a distorted image, without applying calibration frames and that partially bound streaks provide similar results to traditional data, with a mean degradation of 6:2% in right ascension and 42:69% in declination. The methodology developed can also be applied to dedicated SSA sensors to extract data from serendipitous streaks gathered while observing other RSOs

Space Image Processing and Orbit Estimation Using Small Aperture Optical Systems

Angles-only initial orbit determination (AIOD) methods have been used to find the orbit of satellites since the beginning of the Space Race. Given the ever increasing number of objects in orbit today, the need for accurate space situational awareness (SSA) data has never been greater. Small aperture (\u3c 0:5m) optical systems, increasingly popular in both amateur and professional circles, provide an inexpensive source of such data. However, utilizing these types of systems requires understanding their limits. This research uses a combination of image processing techniques and orbit estimation algorithms to evaluate the limits and improve the resulting orbit solution obtained using small aperture systems. Characterization of noise from physical, electronic, and digital sources leads to a better understanding of reducing noise in the images used to provide the best solution possible. Given multiple measurements, choosing the best images for use is a non-trivial process and often results in trying all combinations. In an effort to help autonomize the process, a novel “observability metric” using only information from the captured images was shown empirically as a method of choosing the best observations. A method of identifying resident space objects (RSOs) in a single image using a gradient based search algorithm was developed and tested on actual space imagery captured with a small aperture optical system. The algorithm was shown to correctly identify candidate RSOs in a variety of observational scenarios

Active Perception for Autonomous Systems : In a Deep Space Navigation Scenario

Autonomous systems typically pursue certain goals for an extended amount of time in a self-sustainable fashion. To this end, they are equipped with a set of sensors and actuators to perceive certain aspects of the world and thereupon manipulate it in accordance with some given goals. This kind of interaction can be thought of as a closed loop in which a perceive-reason-act process takes place. The bi-directional interface between an autonomous system and the outer world is then given by a sequence of imperfect observations of the world and corresponding controls which are as well imperfectly actuated. To be able to reason in such a setting, it is customary for an autonomous system to maintain a probabilistic state estimate. The quality of the estimate -- or its uncertainty -- is, in turn, dependent on the information acquired within the perceive-reason-act loop described above. Hence, this thesis strives to investigate the question of how to actively steer such a process in order to maximize the quality of the state estimate. The question will be approached by introducing different probabilistic state estimation schemes jointly working on a manifold-based encapsuled state representation. On top of the resultant state estimate different active perception approaches are introduced, which determine optimal actions with respect to uncertainty minimization. The informational value of the particular actions is given by the expected impact of measurements on the uncertainty. The latter can be obtained by different direct and indirect measures, which will be introduced and discussed. The active perception schemes for autonomous systems will be investigated with a focus on two specific deep space navigation scenarios deduced from a potential mining mission to the main asteroid belt. In the first scenario, active perception strategies are proposed, which foster the correctional value of the sensor information acquired within a heliocentric navigation approach. Here, the expected impact of measurements is directly estimated, thus omitting counterfactual updates of the state based on hypothetical actions. Numerical evaluations of this scenario show that active perception is beneficial, i.e., the quality of the state estimate is increased. In addition, it is shown that the more uncertain a state estimate is, the more the value of active perception increases. In the second scenario, active autonomous deep space navigation in the vicinity of asteroids is investigated. A trajectory and a map are jointly estimated by a Graph SLAM algorithm based on measurements of a 3D Flash-LiDAR. The active perception strategy seeks to trade-off the exploration of the asteroid against the localization performance. To this end, trajectories are generated as well as evaluated in a novel twofold approach specifically tailored to the scenario. Finally, the position uncertainty can be extracted from the graph structure and subsequently be used to dynamically control the trade-off between localization and exploration. In a numerical evaluation, it is shown that the localization performance of the Graph SLAM approach to navigation in the vicinity of asteroids is generally high. Furthermore, the active perception strategy is able to trade-off between localization performance and the degree of exploration of the asteroid. Finally, when the latter process is dynamically controlled, based on the current localization uncertainty, a joint improvement of localization as well as exploration performance can be achieved. In addition, this thesis comprises an excursion into active sensorimotor object recognition. A sensorimotor feature is derived from biological principles of the human perceptual system. This feature is then employed in different probabilistic classification schemes. Furthermore, it enables the implementation of an active perception strategy, which can be thought of as a feature selection process in a classification scheme. It is shown that those strategies might be driven by top-down factors, i.e., based on previously learned information, or by bottom-up factors, i.e., based on saliency detected in the currently considered data. Evaluations are conducted based on real data acquired by a camera mounted on a robotic arm as well as on datasets. It is shown that the integrated representation of perception and action fosters classification performance and that the application of an active perception strategy accelerates the classification process

EBEX: A Balloon-Borne Telescope for Measuring Cosmic Microwave Background Polarization

EBEX is a long-duration balloon-borne (LDB) telescope designed to probe polarization signals in the cosmic microwave background (CMB). It is designed to measure or place an upper limit on the inflationary B-mode signal, a signal predicted by inflationary theories to be imprinted on the CMB by gravitational waves, to detect the effects of gravitational lensing on the polarization of the CMB, and to characterize polarized Galactic foreground emission. The payload consists of a pointed gondola that houses the optics, polarimetry, detectors and detector readout systems, as well as the pointing sensors, control motors, telemetry sytems, and data acquisition and flight control computers. Polarimetry is achieved with a rotating half-wave plate and wire grid polarizer. The detectors are sensitive to frequency bands centered on 150, 250, and 410 GHz. EBEX was flown in 2009 from New Mexico as a full system test, and then flown again in December 2012 / January 2013 over Antarctica in a long-duration flight to collect scientific data. In the instrumentation part of this thesis we discuss the pointing sensors and attitude determination algorithms. We also describe the real-time map making software, "QuickLook", that was custom-designed for EBEX. We devote special attention to the design and construction of the primary pointing sensors, the star cameras, and their custom-designed flight software package, "STARS" (the Star Tracking Attitude Reconstruction Software). In the analysis part of this thesis we describe the current status of the post-flight analysis procedure. We discuss the data structures used in analysis and the pipeline stages related to attitude determination and map making. We also discuss a custom-designed software framework called "LEAP" (the LDB EBEX Analysis Pipeline) that supports most of the analysis pipeline stages

Machine learning for the harsh environment: applications in sea ice classification and satellite magnetic fault recovery

This thesis presents the development of two machine learning navigation modules for harsh environment applications. The first application investigates semantic segmentation using neural networks for sea ice detection and classification in polar oceans. Two popular generic architectures, SegNet and PSPNet101 are used to segment images. Transfer learning is performed using two custom datasets, one with four classes: ice, ocean, vessel, and sky, i.e., sea ice detection dataset, and the second with eight classes: ocean, vessel, sky, lens artifacts, first-year ice, new ice, grey ice, and multiyear ice, i.e., sea ice classification dataset. The Nathaniel B. Palmer imagery, which captured 2-month footage of the icebreaker completing an Antarctic expedition was used in the creation of both datasets. A subset of the dataset was labeled to generate a 240-image training set for sea ice detection achieving an accuracy of 98% classification for the 26-image test set. The sea ice classification dataset consists of 1,090 labeled images achieving accuracies of 98.3% or greater for all ice types for the 104-image test set. The second application investigates a new attitude error parameterization and a machine learning regression model for small satellite attitude fault recovery systems experiencing magnetometer bias faults. A simulation environment is developed to mimic an orbit of the international space station, and simulates both the magnetometer and the fine sun sensor on-board a small satellite. A right quaternion error parameterization is presented to ensure consistent error bound growth during the eclipse period of orbits where only a subset of sensor data is available. Using the improved error bounds a fault detection method using Mahalanobis distance is implemented to flag any faults in the system. After the fault detection, the fault recovery uses a regression sliding window optimizer to determine the unknown magnetometer bias that the sensor encounters. The proposed method demonstrates improved root mean squared error and error bound consistency achievable using the right error formulation for magnetic bias fault detection and recovery applications of small satellites

Advanced Strategies for Robot Manipulators

Amongst the robotic systems, robot manipulators have proven themselves to be of increasing importance and are widely adopted to substitute for human in repetitive and/or hazardous tasks. Modern manipulators are designed complicatedly and need to do more precise, crucial and critical tasks. So, the simple traditional control methods cannot be efficient, and advanced control strategies with considering special constraints are needed to establish. In spite of the fact that groundbreaking researches have been carried out in this realm until now, there are still many novel aspects which have to be explored

sコールドガスジェットスラスタとリアクションホイールを利用した超小型衛星のための姿勢・軌道制御系の開発と軌道上評価

Tohoku University吉田和哉課

A Bibliography of NPS Space Systems Related Student Research, 2013-2022

Dudley Knox Library, Naval Postgraduate School.Approved for Public Release; distribution is unlimite

Autonomous Space Surveillance for Arbitrary Domains

Space is becoming increasingly congested every day and the task of accurately tracking satellites is paramount for the continued safe operation of both manned and unmanned space missions. In addition to new spacecraft launches, satellite break-up events and collisions generate large amounts of orbital debris dramatically increasing the number of orbiting objects with each such event. In order to prevent collisions and protect both life and property in orbit, accurate knowledge of the position of orbiting objects is necessary. Space Domain Awareness (SDA) used interchangeably with Space Situational Awareness (SSA), are the names given to the daunting task of tracking all orbiting objects. In addition to myriad objects in low-earth-orbit (LEO) up to Geostationary (GEO) orbit, there are a growing number of spacecraft in cislunar space expanding the task of cataloguing and tracking space objects to include the whole of the earth-moon system. This research proposes a series of algorithms to be used in autonomous SSA for earth-orbiting and cislunar objects. The algorithms are autonomous in the sense that once a set of raw measurements (images in this case) are input to the algorithms, no human in the loop input is required to produce an orbit estimate. There are two main components to this research, an image processing and satellite detection component, and a dynamics modeling component for three-body relative motion. For the image processing component, resident space objects, (commonly referred to as RSOs) which are satellites or orbiting debris are identified in optical images. Two methods of identifying RSOs in a set of images are presented. The first method autonomously builds a template image to match a constellation of satellites and proceeds to match RSOs across a set of images. The second method utilizes optical flow to use the image velocities of objects to differentiate between stars and RSOs. Once RSOs have been detected, measurements are generated from the detected RSO locations to estimate the orbit of the observed object. The orbit determination component includes multiple methods capable of handling both earth-orbiting and cislunar observations. The methods used include batch-least squares and unscented Kalman filtering for earth-orbiting objects. For cislunar objects, a novel application of a particle swarm optimizer (PSO) is used to estimate the observed satellite orbit. The PSO algorithm ingests a set of measurements and attempts to match a set of virtual particle measurements to the truth measurements. The PSO orbit determination method is tested using both MATLAB and Python implementations. The second main component of this research develops a novel linear dynamics model of relative motion for satellites in cislunar space. A set of novel linear relative equations of motion are developed with a semi-analytical matrix exponential method. The motion models are tested on various cislunar orbit geometries for both the elliptical restricted three-body problem (ER3BP) and the circular restricted three-body problem (CR3BP) through MATLAB simulations. The linear solution method\u27s accuracy is compared to the non-linear equations of relative motion and are seen to hold to meter level accuracy for deputy position for a variety of orbits and time-spans. Two applications of the linearized motion models are then developed. The first application defines a differential corrector to compute closed relative motion trajectories in a relative three-body frame. The second application uses the exponential matrix solution for the linearized equations of relative motion to develop a method of initial relative orbit determination (IROD) for the CR3BP