Use of Hierarchical Mixtures of Experts to Detect Resident Space Object Attitude

Space Situational Awareness (SSA) involves detecting, tracking, identifying and characterizing resident space objects (RSOs). Electro-optical measurements can be used to estimate important characteristics, such as the size, shape, configuration, rotational dynamics (attitude and angular velocity), and surface properties such as specular and diffuse albedo (reflectivity) of a resident space object (RSO). In addition, estimated features can be used to match or discriminate objects or classes of objects and to identify their behavior. Under a Phase I SBIR sponsored by Air Force Research Laboratory (AFRL), Emergent Space Technologies, Inc. investigated the use of a Hierarchical Mixture of Experts (HME) to process electro-optical measurements to determine an RSO’s attitude profile. This paper discusses the mathematical background of the HME; the assumptions, test scenarios, and results of processing simulated apparent magnitude and angles data including experiments to tune the HME learning rate parameter. The results show that the HME is capable of identifying and distinguishing between nadir-pointing, sunpointing, and spinning objects even though none of the experts in the HME is directly estimating attitude. This paper also shows how the learning parameter selection impacts HME performance.