Holistic RSO Space Situational Awareness Algorithms

ABSTRACT: A holistic RSO space situational awareness algorithm will be developed that integrates Finite Set Statistics (FISST) with Hiearchical Mixtures of Experts (HME) and Multiple Model Adaptive Estimation (MMAE). Advanced orbit determination filters such as Splitting Gaussian Mixture Unscented Kalman Filters will perform as banks of experts to test which RSO and environment models best fit space surveillance observation data that includes electro-optical and radar angles, range, OCS, and RCS. The FISST/HME/MMAE integrated algorithm will detect changes in RSO and environmental characteristics such as spacecraft size, reflectivity, and configuration or drag and solar radiation pressure. Avanced numerical integration techniques and GPU parallelization will ensure algorithm speed and robustness with the goal of maintaining a future space catalog approaching 100,000 objects. RSO Detection, Tracking, Identification, and Characterization will be improved by the integrated set of algorithms, while scarce Air Force space surveillance sensors will be optimally scheduled for maximum Space Situation Awareness for commercial entities, military commanders, and JSpOC operators. The integrated algorithms will have a rigorous math and physics base, will be posed in a uniform framework, and will accurately model uncertainty. BENEFIT: Improved detection, tracking, identification, and characterization of space objects using data fusion from multiple sensors. Optimal scheduling of Air Force space surveillance sensors including ground and space based electro-optical systems. More accurate ephemerides development leading to earlier prediction of potential conjunctions, requiring less propellant for avoidance maneuvers, and thus increasing commercial spacecraft operational lifetimes. Increased automation of JSpOC operations, combined with increased situation awareness for all space operators. Improved speed, accuracy, and robustness of all computational processes.