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Aerospace Vehicle Navigation And Control System Comprising Terrestrial Illumination Matching Module For Determining Aerospace Vehicle Position And Attitude
Get PDFThe present invention relates to an aerospace vehicle navigation and control system comprising a terrestrial illumination matching module for determining spacecraft position and attitude. The method permits aerospace vehicle position and attitude determinations using terrestrial lights using an Earth-pointing camera without the need of a dedicated sensor to track stars, the sun, or the horizon. Thus, a module for making such determinations can easily and inexpensively be made onboard an aerospace vehicle if an Earth-pointing sensor, such as a camera, is present
Impact of Dilute Amounts of Fission Products on the Mechanical Behavior of Ni
No full textission products may interact with structural materials in various nuclear energy applications and cause their mechanical performance to deteriorate. Therefore, it is important to study the effects of different fission products on the mechanical properties of structural materials. In this work, nickel was chosen as a model structural material system and dilute amounts of uranium and fission product impurities X = (Tc, Te, Sb, Ce, Eu, and U) up to 4 at % were used. Density functional theory (DFT) calculations were utilized to assess the effects of these substitutional impurities on the elastic behavior of the metal. Additionally, DFT was used to investigate some aspects of plastic response by computing the generalized stacking fault energies on the {111} ⟨112⟩ slip system for all alloying elements and at varying distances away from the stacking fault plane. None of the dopants satisfied the Pugh or Pettifor criteria for embrittlement, and alloying with Tc led to a slight increase in the elasticity of nickel. The phenomenon of Suzuki segregation was observed for all alloying elements, and there was consequently a significant reduction in the intrinsic stacking fault energy. Finally, and based on the analysis of the stacking fault energies, dopants generally led to softening the nickel (except for Tc and Ce), and all of the dopants were correlated with a loss of ductility (except Eu). These findings may be useful to consider in the design of next-generation reactors and nuclear waste management system
Analysis of a Distributed Command-and-Control Algorithm to Implement Mosaic Warfare
No full textRecognizing that communication between assets may be possible locally but not globally (e.g., due to disruptions to a communication network), Mosaic Warfare requires the movement and operation of multiple, dispersed assets in smaller groups (i.e., tiles), within which exist hierarchical, functional relationships between assets. This research sets forth and evaluates a hierarchical asset tiling and routing heuristic (HATRH) to implement Mosaic Warfare for an enterprise of aerial assets comprised of airborne sensors, command-and-control aircraft, and strike aircraft seeking to move toward and destroy a set of stationary targets. The HATRH is comprised of three, iteratively applied algorithms: a grouping algorithm to cluster assets into functional tiles, and two algorithms respectively related to group movement and individual asset movement. Embedded within the latter two algorithms are user-determined parameters that roughly correspond to group and individual asset agency within the mosaic. Extensive testing examined the effect of these parameters and asset density for three different operational scenario designs, and with comparison to optimal (i.e., efficient) asset utilization via two Price of Anarchy (POA) inspired metrics. Results showed the user-defined parameter corresponding to individual asset agency notably influenced both average munition expenditures and the average distance traveled by assets. In the scenario wherein assets initially surround adversary targets, both the individual and group agency user-defined parameters influence operational efficiency, in terms of munitions expended and fuel consumed
Revealing Bridges in Social Networks
No full textSocial groups and networks are ubiquitous, and each network member typically has a role or roles in associated interactions. One such member identified in social network literature is a bridge, which connects two or more groups, and is an element of only one of the groups; inferred from literature (e.g., Granovetter, 1973; Rogers and Kincaid, 1981) is the notion that bridges play a key role in networks. At different times and for various reasons, network and group components may be unrevealed. An example is a terrorist network; the terrorists may know who comprises their network, but a military or security organization that is attempting to dismantle the organization is unlikely to know all of the individuals and/or their interactions and roles. Consequently, the ability to characterize and detect bridges could be valuable in the national security structure\u27s efforts since such members can play a key role in networks. This article addresses the associated problem: Given a network of groups, for which the information about each group consists of individuals, their attributes, and (only) their intra-group relations, identify which individual (or individuals) is a bridge node. Additionally, an approach is presented for detecting that a bridge is missing from a group, i.e., inferring the existence of a bridge from group data that does not contain the bridge in its membership, the bridge\u27s attributes, nor the bridge\u27s contacts. Accordingly, a method for recreating the ground truth network once a bridge\u27s existence has been detected is given. Lastly, the potential application of these approaches to networks other than social networks is discussed
Impact of Operational Ladar Occlusions on Point Cloud Instance Segmentation
Get PDFData exploitation techniques are the enabler for technological advancements in military ISR applications of ladar ISR. By identifying instances of military objects in observed scenes, point cloud deep learning models can unlock new standards of real-time information delivery to warfighters. Although current deep learning training datasets do not include real-world collection occlusions consistent with military applications, this research characterizes SPT model performance by adding occlusions to the DALESObjects dataset via artificial flyby simulations.We find that a baseline model trained on unoccluded data suffers performance degradation on both semantic and instance segmentation tasks when evaluated on occluded data, but that the effects can be minimized by collecting maximum angular coverage of the scene, especially at balanced observation elevations that stay away from extremely shallow or steep angles of collection. Beyond the most occluded edge cases, the model’s ability to identify vehicle instances within a scene was largely disconnected from the percentage of vehicle points observed. This suggests that identifying objects of interest within a scene can be done with a relatively sparse representation of that scene. Specialized models trained on occluded data exhibit increased performance on representative data compared to the baseline model. Matching training data to testing data maximizes performance, with degradations as testing data diverges in elevation or decreases in angular coverage. Increasing angular coverage can result in increased model performance but not above the performance of a network specially trained on that level of angular coverage
Innovation Challenges in the Air Force SBIR Program: From the Small Businesses’ Perspective
Get PDFEvery year the United States invests $3.2 billion in the Small Business Innovation Research (SBIR) program to promote innovation among the nation’s small businesses. Half of this investment is from the DoD. This research considers the challenges faced by small businesses innovating with the DoD, particularly those awarded SBIR contracts with the United States Air Force. The authors surveyed 286 unique small businesses that were previously awarded an Air Force SBIR contract. By asking the survey respondents open-ended questions and categorizing their responses, they pinpoint unaddressed challenges from the small business perspective. By categorizing survey responses through Qualitative Content Analysis, they further identify five categories of challenges: Solver-Seeker Disconnect, Funding, Engagement, Processes, and Seeker Education. With this new insight, the authors seek to inform SBIR policies and improve the program’s effectiveness
Machine Visual Perception from Sim-to-real Transfer Learning for Autonomous Docking Maneuvers
Get PDFThis paper presents a comprehensive approach to enhancing autonomous docking maneuvers through machine visual perception and sim-to-real transfer learning. By leveraging relative vectoring techniques, we aim to replicate the human ability to execute precise docking operations. Our study focuses on autonomous aerial refueling as a use case, demonstrating significant advancements in relative navigation and object detection. We introduce a novel method for aligning digital twins using fiducial targets and motion capture data, which facilitates accurate pose estimation from real-world imagery. Additionally, we develop cost-efficient annotation automation techniques for generating high-quality You Only Look Once training data. Experimental results indicate that our transfer learning methodologies enable accurate and reliable relative vectoring in real-world conditions, achieving error margins of less than 3 cm at contact (when vehicles are approximately 4 m from the camera) and maintaining performance at over 56 fps. The research findings underscore the potential of augmented reality and scene augmentation in improving model generalization and performance, bridging the gap between simulation and real-world applications. This work lays the groundwork for deploying autonomous docking systems in complex and dynamic environments, minimizing human intervention and enhancing operational efficiency
