Transformer-based Atmospheric Density Forecasting

As the peak of the solar cycle approaches in 2025 and the ability of a single geomagnetic storm to significantly alter the orbit of Resident Space Objects (RSOs), techniques for atmospheric density forecasting are vital for space situational awareness. While linear data-driven methods, such as dynamic mode decomposition with control (DMDc), have been used previously for forecasting atmospheric density, deep learning-based forecasting has the ability to capture nonlinearities in data. By learning multiple layer weights from historical atmospheric density data, long-term dependencies in the dataset are captured in the mapping between the current atmospheric density state and control input to the atmospheric density state at the next timestep. This work improves upon previous linear propagation methods for atmospheric density forecasting, by developing a nonlinear transformer-based architecture for atmospheric density forecasting. Empirical NRLMSISE-00 and JB2008, as well as physics-based TIEGCM atmospheric density models are compared for forecasting with DMDc and with the transformer-based propagator.Comment: Conference: 24th Advanced Maui Optical and Space Surveillance Technologies At: Maui, Hawaii, United State

Influences of Space Weather Forecasting Uncertainty on Satellite Conjunction Assessment

A significant increase in the number of anthropogenic objects in Earth orbit has necessitated the development of satellite conjunction assessment and collision avoidance capabilities for new spacecraft. Neutral mass density variability in the thermosphere, driven by enhanced geomagnetic activity and solar EUV absorption, is a major source of satellite propagation error. This work investigates the impacts of space weather driver forecasting uncertainty on satellite drag and collision avoidance maneuver decision-making. Since most operational space weather driver forecasts do not offer an uncertainty assessment, the satellite operator community is left to make dangerous assumptions about the trustworthiness of the forecast models they use to perform satellite state propagation. Climatological persistence-based forecast models are developed for F10.7 and Kp. These models accurately capture the heteroscedastic and, at times, highly non-Gaussian uncertainty distribution on forecasts of the drivers of interest. A set of realistic satellite conjunction scenarios is simulated to demonstrate the contributions of space weather driver forecast uncertainty on the probability of collision and maneuver decisions. Improved driver forecasts, especially forecasts of F10.7, are demonstrated to be very useful for enabling durable maneuver decisions with additional lead time (up to 24 hr for the period examined), though the improvement depends on the specific conjunction scenario of interest

Multiple Target Tracking Using Random Finite Sets

University of Minnesota Ph.D. dissertation.January 2021. Major: Aerospace Engineering. Advisor: Richard Linares. 1 computer file (PDF); xi, 271 pages.Multiple target tracking (MTT) plays a crucial role in guidance, navigation, and control of autonomous systems. However, it presents challenges in terms of computational complexity, measurement-to-track association ambiguity, clutter, and miss detection. The first half of the dissertation looks into multiple extended target tracking on a moving platform using cameras and a Light Detection and Ranging (LiDAR) scanner. A Bayesian framework is first designed for simultaneous localization and mapping and detection of dynamic objects. Two random finite sets filters are developed to track the extracted dynamic objects. First, the Occupancy Grid (OG) Gaussian Mixture (GM) Probability Hypothesis Density (PHD) filter jointly tracks the target kinematic states and a modified occupancy grid map representation of the target shape. The OG-GM-PHD filter successfully reconstructed the shape of the targets and resulted in a lower Optimal Sub-Pattern Assignment (OSPA) error metric than the traditional GM-PHD filter. The second MTT filter (Classifying Multiple Model (CMM) Labeled Multi Bernoulli (LMB)) is developed to leverage class-dependent motion characteristics. It fuses classification data from images to point cloud and incorporates object class probabilities into the tracked target states. This allows for better measurement-to-track associations and usage of class-dependent motion and birth models. The CMM-LMB filter is evaluated on KITTI dataset and simulated data from CARLA simulator. The CMM-LMB filter leads to a lower OSPA error metric than the Multiple Model LMB and LMB filters in both cases. The second half looks into sensor management for MTT using a sensor with a narrow field of view and a finite action slew rate. The sensor management for space situational awareness (SSA) is chosen as an application scenario. Classical sensor management algorithm for SSAtends to only consider the immediate reward. In this dissertation, deep reinforcement learning (DRL) agents are developed to overcome the combinatorial increase in problem size for long-term sensor tasking problems. A custom environment for SSA sensor tasking was developed in order to train and evaluate the DRL agents. The DRL agents are trained using Proximal Policy Optimization with Population Based Training and are able to outperform traditional myopic policies

Optimal Tasking of Ground-Based Sensors for Space Situational Awareness Using Deep Reinforcement Learning

Space situational awareness (SSA) is becoming increasingly challenging with the proliferation of resident space objects (RSOs), ranging from CubeSats to mega-constellations. Sensors within the United States Space Surveillance Network are tasked to repeatedly detect, characterize, and track these RSOs to retain custody and estimate their attitude. The majority of these sensors consist of ground-based sensors with a narrow field of view and must be slew at a finite rate from one RSO to another during observations. This results in a complex combinatorial problem that poses a major obstacle to the SSA sensor tasking problem. In this work, we successfully applied deep reinforcement learning (DRL) to overcome the curse of dimensionality and optimally task a ground-based sensor. We trained several DRL agents using proximal policy optimization and population-based training in a simulated SSA environment. The DRL agents outperformed myopic policies in both objective metrics of RSOs’ state uncertainties and the number of unique RSOs observed over a 90-min observation window. The agents’ robustness to changes in RSO orbital regimes, observation window length, observer’s location, and sensor properties are also examined. The robustness of the DRL agents allows them to be applied to any arbitrary locations and scenarios

Informativeness of quarterly earnings in an asymmetric information environment.

The main aim is extend findings of Landsman and Maydew in an asymmetric information environment to see if quarterly earnings is of value relevance with the introduction of other information sources like announcements and analyst followings

Unmanned systems in integrating cross-domain naval fires

Systems Engineering Capstone Project ReportThe ability to communicate and transmit targeting data via the electromagnetic spectrum is crucial to the Navy's ability to fight. However, in recent years, potential adversaries have significantly advanced their electronic warfare capabilities, obtaining an ability to interfere with the Navy's use of the electromagnetic spectrum during operations in contested environments. SEA23 investigates concepts of operation focusing on future potential electromagnetic-spectrum warfighting capabilities in the 2025Ð2030 timeframe. Specifically, we explore these capabilities using modular unmanned and manned platforms capable of carrying communications and data suites to enable cross-domain targeting information in support of tactical offensive operations in a contested, denied, degraded, intermittent, and limited-bandwidth environment. This project focuses on developing a system-of-systems architecture and analyzing alternatives to provide potential solutions while developing the associated concepts of operation. We recommend an architecture based on Link 16 and organic rotary-wing unmanned aerial vehicles to transfer sensor to shooter data in demanding and contested environments.http://archive.org/details/unmannedsystemsi1094549381Approved for public release; distribution is unlimited

Dynamic Altruistic Cooperation Within Breast Tumors

10.1186/s12943-023-01896-72220