Systems & Technology Research LLC — Department of Defense SBIR Phase I: AF151-097

Description

ABSTRACT:The objective of this proposed Phase I SBIR is to develop Space Situational Awareness (SSA) algorithms for maximizing detection probability and state estimation accuracy of Earth orbiting space objects, including active as well as inactive satellites, and breakups and debris, using a constellation of space-based multi-sensor platforms. Systems & Technology Research (STR) proposes to provide a closed-loop solution that consists of both optimal estimation and optimal control. Since the estimation problem is to estimate an unknown number of object states, the problem becomes a dynamical state estimation of a random set, which we solve using a novel jump Markov, feature-aided multiple-hypothesis tracking (MHT) algorithm. We will use the jump Markov model for detecting and adjusting for satellite maneuvers, while the feature-aided MHT exploits multi-modal, multiple sensors on the space-based platforms. This MHT algorithm provides an optimal solution for detecting, tracking, identifying, and characterizing space objects, given a sensor schedule. Then an information-metric-driven sensor resource management (SRM) algorithm will evaluate the MHT database output and solve the optimal SRM sensor schedule problem dynamically, to ensure close-loop optimality. The Phase I goal is a technical concept demonstration, and its focus will be the GEO belt monitoring.BENEFIT:Using space-based SSA platforms will enable almost continuous SSA operations, removing space and temporal gaps of space object observations by conventional ground-based Space Surveillance Network (SSN) sensors. The potential commercial applications include space monitoring applications, especially the monitoring of the commercial geostationary satellite operations, mainly broadcasting and communication applications. In the future, commercial SSA monitoring will become necessary for other private-sector space activities, including space exploration and sightseeing.