Rao-Blackwellization for Adaptive Gaussian Sum Nonlinear Model Propagation

When dealing with imperfect data and general models of dynamic systems, the best estimate is always sought in the presence of uncertainty or unknown parameters. In many cases, as the first attempt, the Extended Kalman filter (EKF) provides sufficient solutions to handling issues arising from nonlinear and non-Gaussian estimation problems. But these issues may lead unacceptable performance and even divergence. In order to accurately capture the nonlinearities of most real-world dynamic systems, advanced filtering methods have been created to reduce filter divergence while enhancing performance. Approaches, such as Gaussian sum filtering, grid based Bayesian methods and particle filters are well-known ∗Aerospace Engineer, Guidance, Navigation and Control Hardware and Components Branch. Email: [email protected]. Member AIAA. †CUBRC Professor in Space Situational Awareness, Department of Mechanical & Aerospace Engineering. Email: [email protected]. Associate Fellow AIAA. ‡Engineer, Networked Sensing and Fusion Branch. Email: [email protected]. §Student, Department of Mechanical & Aerospace Engineering. Email: [email protected]. ¶Associate Professor, Department of Mechanical & Aerospace Engineering. Email: [email protected].