Adaptive Estimation and Heuristic Optimization of Nonlinear Spacecraft Attitude Dynamics

For spacecraft conducting on-orbit operations, changes to the structure of the spacecraft are not uncommon. These planned or unanticipated changes in inertia properties couple with the spacecraft\u27s attitude dynamics and typically require estimation. For systems with time-varying inertia parameters, multiple model adaptive estimation (MMAE) routines can be utilized for parameter and state estimates. MMAE algorithms involve constructing a bank of recursive estimators, each assuming a different hypothesis for the systems dynamics. This research has three distinct, but related, contributions to satellite attitude dynamics and estimation. In the first part of this research, MMAE routines employing parallel banks of unscented attitude filters are applied to analytical models of spacecraft with time-varying mass moments of inertia (MOI), with the objective of estimating the MOI and classifying the spacecraft\u27s behavior. New adaptive estimation techniques were either modified or developed that can detect discontinuities in MOI up to 98 of the time in the specific problem scenario.Second, heuristic optimization techniques and numerical methods are applied to Wahba\u27s single-frame attitude estimation problem,decreasing computation time by an average of nearly 67 . Finally, this research poses MOI estimation as an ODE parameter identification problem, achieving successful numerical estimates through shooting methods and exploiting the polhodes of rigid body motion with results, on average, to be within 1 to 5 of the true MOI values

A novel satellite mission concept for upper air water vapour, aerosol and cloud observations using integrated path differential absorption LiDAR limb sounding

We propose a new satellite mission to deliver high quality measurements of upper air water vapour. The concept centres around a LiDAR in limb sounding by occultation geometry, designed to operate as a very long path system for differential absorption measurements. We present a preliminary performance analysis with a system sized to send 75 mJ pulses at 25 Hz at four wavelengths close to 935 nm, to up to 5 microsatellites in a counter-rotating orbit, carrying retroreflectors characterized by a reflected beam divergence of roughly twice the emitted laser beam divergence of 15 µrad. This provides water vapour profiles with a vertical sampling of 110 m; preliminary calculations suggest that the system could detect concentrations of less than 5 ppm. A secondary payload of a fairly conventional medium resolution multispectral radiometer allows wide-swath cloud and aerosol imaging. The total weight and power of the system are estimated at 3 tons and 2,700 W respectively. This novel concept presents significant challenges, including the performance of the lasers in space, the tracking between the main spacecraft and the retroreflectors, the refractive effects of turbulence, and the design of the telescopes to achieve a high signal-to-noise ratio for the high precision measurements. The mission concept was conceived at the Alpbach Summer School 2010

Technology for Low Resolution Space Based RSO Detection and Characterisation

Space Situational Awareness (SSA) refers to all activities to detect, identify and track objects in Earth orbit. SSA is critical to all current and future space activities and protect space assets by providing access control, conjunction warnings, and monitoring status of active satellites. Currently SSA methods and infrastructure are not sufficient to account for the proliferations of space debris. In response to the need for better SSA there has been many different areas of research looking to improve SSA most of the requiring dedicated ground or space-based infrastructure. In this thesis, a novel approach for the characterisation of RSO’s (Resident Space Objects) from passive low-resolution space-based sensors is presented with all the background work performed to enable this novel method. Low resolution space-based sensors are common on current satellites, with many of these sensors being in space using them passively to detect RSO’s can greatly augment SSA with out expensive infrastructure or long lead times. One of the largest hurtles to overcome with research in the area has to do with the lack of publicly available labelled data to test and confirm results with. To overcome this hurtle a simulation software, ORBITALS, was created. To verify and validate the ORBITALS simulator it was compared with the Fast Auroral Imager images, which is one of the only publicly available low-resolution space-based images found with auxiliary data. During the development of the ORBITALS simulator it was found that the generation of these simulated images are computationally intensive when propagating the entire space catalog. To overcome this an upgrade of the currently used propagation method, Specialised General Perturbation Method 4th order (SGP4), was performed to allow the algorithm to run in parallel reducing the computational time required to propagate entire catalogs of RSO’s. From the results it was found that the standard facet model with a particle swarm optimisation performed the best estimating an RSO’s attitude with a 0.66 degree RMSE accuracy across a sequence, and ~1% MAPE accuracy for the optical properties. This accomplished this thesis goal of demonstrating the feasibility of low-resolution passive RSO characterisation from space-based platforms in a simulated environment

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

2007 Annual Report of the Graduate School of Engineering and Management, Air Force Institute of Technology

The Graduate School\u27s Annual Report highlights research focus areas, new academic programs, faculty accomplishments and news, and provides top-level sponsor-funded research data and information

On Thermospheric Density and Wind Modeling Driven by Satellite Observations

The thermosphere is home to a plethora of orbiting objects ranging in size from flecks of paint to modular spacecraft with masses on the order of thousands of kilograms. The region spans hundreds of kilometers in vertical extent, from ∼100 km where fixed-wing flight by aerodynamic lift is unsupportable, out to ∼500-700 km, depending on solar activity, where the particle density is so sparse that the atmosphere can no longer be treated as a fluid. The thermosphere is subject to dynamical energy input from radiation and magnetic sources that make quantifying its dynamics a nontrivial endeavor. This is particularly a challenge during geomagnetic storms, where increased magnetic activity primarily at high-latitudes drives global heating, traveling atmospheric disturbances, and intense winds throughout the thermosphere. Modeling of the neutral density and horizontal winds is a challenging endeavor for these conditions, and it is vital not only for understanding the physics of neutral atmospheres, but also for the practical purposes of improving orbit prediction, as the thermosphere is home to an increasing number of satellite missions, in addition to being the abode of astronauts. Various atmospheric models have been constructed and developed over decades in order to model the thermosphere, with the most prominent being the empirical models Mass Spectrometer and Incoherent Scatter Radar MSIS-00, Jacchia-Bowman JB-2008, and Drag-Temperature Model DTM-2013, which are primarily used to model the neutral density, and GITM, a physics-based model capable of modeling atmospheric electrodynamics and investigating thermospheric winds. This dissertation focuses on three important means by which the interplay between satellite measurements and atmospheric models can drive scientific development for use in satellite mission operations and model development outright. In order to reduce the empirical mode bias during storms, we created the Multifaceted Optimization Algorithm (MOA), a method to modify the drivers of the models by comparing actual and simulated orbits through the model to reduce the errors. Applying MOA to the MSIS-00 model allowed a decrease in model error from 25% to 10% in the event that was examined, and represents an easy-to-implement technique that can use publicly available two-line-element orbital data. A superposed epoch analysis of three empirical density models shows persistent storm-time overestimation by JB-2008 and underestimation DTM-2013 by MSIS-00 for more intense geomagnetic storms that may be addressed with a Dst-based calibration, and a statistical analysis of GITM horizontal winds indicates the best performance in the polar and auroral zones and difficulty capturing seasonality. The work contained in this dissertation aims to provide techniques and analysis tools to improve density and wind model performance, in order to support satellite mission operators and atmospheric research. Ultimately, it demonstrates that simple tools and methods can be utilized to generate significant results and scientific insight, serving to augment and supplement more computationally intensive and cost-prohibitive strategies for investigating the thermospheric environment.PHDClimate and Space Sciences and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169999/1/branddan_1.pd

Satellite swarms for auroral plasma science

With the growing accessibility of space, this thesis work sets out to explore space-based swarms to do multipoint magnetometer measurements of current systems embedded within the Aurora Borealis as an initial foray into concepts for space physics applications using swarms of small spacecraft. As a pathfinder, ANDESITE---a 6U CubeSat with eight deployable picosatellites---was built as part of this research. The mission will fly a local network of magnetometers above the Northern Lights. With the spacecraft due to launch on an upcoming ELaNa mission, here we discuss the details of the science motivation, the mathematical framework for current field reconstruction, the particular hardware implementation selected, the calibration procedures, and the pragmatic management needed to realize the spacecraft. After describing ANDESITE and defining its capability, we also propose a follow-on that uses propulsive nodes in a swarm, allowing measurements that can adaptively change to capture the physical phenomena of interest. To do this a flock of satellites needs to fall into the desired formation and maintain it for the duration of the science mission. A simple optimal controller is developed to model the deployment of the satellites. Using a Monte Carlo approach for the uncertain initial conditions, we bound the fuel cost of the mission and test the feasibility of the concept. To illustrate the system analysis needed to effectively design such swarms, this thesis also develops a framework that characterizes the spatial frequency response of the kilometer-scale filter created by the swarm as it flies through various current density structures in the ionospheric plasma. We then subjugate a nominal ANDESITE formation and the controlled swarm specified to the same analysis framework. The choice of sampling scheme and rigorous basic mathematical analysis are essential in the development of a multipoint-measurement mission. We then turn to a novel capability exploiting current trends in the commercial industry. Magnetometers deployed on the largest constellation to date are leveraged as a space-based magnetometer network. The constellation, operated by Planet Labs Inc., consists of nearly 200 satellites in two polar sun-synchronous orbits, with median spacecraft separations on the order of 375 km, and some occasions of opportunity providing much closer spacing. Each spacecraft contains a magneto-inductive magnetometer, able to sample the ambient magnetic field at 0.1 Hz to 10 Hz with <200 nT sensitivity. A feasibility study is presented wherein seven satellites from the Planet constellation were used to investigate space-time patterns in the current systems overlying an active auroral arc over a 10-minute interval. Throughout the this work advantages, limitations, and caveats in exploiting networks of lower quality magnetometers are discussed, pointing out the path forward to creating a global network that can monitor the space environment

Ionospheric Multi-Spacecraft Analysis Tools

This open access book provides a comprehensive toolbox of analysis techniques for ionospheric multi-satellite missions. The immediate need for this volume was motivated by the ongoing ESA Swarm satellite mission, but the tools that are described are general and can be used for any future ionospheric multi-satellite mission with comparable instrumentation. In addition to researching the immediate plasma environment and its coupling to other regions, such a mission aims to study the Earth’s main magnetic field and its anomalies caused by core, mantle, or crustal sources. The parameters for carrying out this kind of work are examined in these chapters. Besides currents, electric fields, and plasma convection, these parameters include ionospheric conductance, Joule heating, neutral gas densities, and neutral winds.

Immunity-Based Framework for Autonomous Flight in GPS-Challenged Environment

In this research, the artificial immune system (AIS) paradigm is used for the development of a conceptual framework for autonomous flight when vehicle position and velocity are not available from direct sources such as the global navigation satellite systems or external landmarks and systems. The AIS is expected to provide corrections of velocity and position estimations that are only based on the outputs of onboard inertial measurement units (IMU). The AIS comprises sets of artificial memory cells that simulate the function of memory T- and B-cells in the biological immune system of vertebrates. The innate immune system uses information about invading antigens and needed antibodies. This information is encoded and sorted by T- and B-cells. The immune system has an adaptive component that can accelerate and intensify the immune response upon subsequent infection with the same antigen. The artificial memory cells attempt to mimic these characteristics for estimation error compensation and are constructed under normal conditions when all sensor systems function accurately, including those providing vehicle position and velocity information. The artificial memory cells consist of two main components: a collection of instantaneous measurements of relevant vehicle features representing the antigen and a set of instantaneous estimation errors or correction features, representing the antibodies. The antigen characterizes the dynamics of the system and is assumed to be correlated with the required corrections of position and velocity estimation or antibodies. When the navigation source is unavailable, the currently measured vehicle features from the onboard sensors are matched against the AIS antigens and the corresponding corrections are extracted and used to adjust the position and velocity estimation algorithm and provide the corrected estimation as actual measurement feedback to the vehicle’s control system. The proposed framework is implemented and tested through simulation in two versions: with corrections applied to the output or the input of the estimation scheme. For both approaches, the vehicle feature or antigen sets include increments of body axes components of acceleration and angular rate. The correction feature or antibody sets include vehicle position and velocity and vehicle acceleration adjustments, respectively. The impact on the performance of the proposed methodology produced by essential elements such as path generation method, matching algorithm, feature set, and the IMU grade was investigated. The findings demonstrated that in all cases, the proposed methodology could significantly reduce the accumulation of dead reckoning errors and can become a viable solution in situations where direct accurate measurements and other sources of information are not available. The functionality of the proposed methodology and its promising outcomes were successfully illustrated using the West Virginia University unmanned aerial system simulation environment

Ionosphere Monitoring with Remote Sensing

This book focuses on the characterization of the physical properties of the Earth’s ionosphere, contributing to unveiling the nature of several processes responsible for a plethora of space weather-related phenomena taking place in a wide range of spatial and temporal scales. This is made possible by the exploitation of a huge amount of high-quality data derived from both remote sensing and in situ facilities such as ionosondes, radars, satellites and Global Navigation Satellite Systems receivers