The objective of this project is to establish a sparse grid filtering framework for accurate estimation of nonlinear and non-Gaussian dynamic systems under computational constraints. This research is motivated by challenging situational awareness problems. Situational awareness is concerned with what is happening in the environment and what will happen in the future. It is critical to decision making in a variety of domains containing large uncertainties and rich dynamics. The research approach is to use a high-accuracy sparse grid as an efficient and accurate representation of non-Gaussian uncertainty and recursively propagate and update the sparse grid. The research will result in a unified point-based filtering framework, computationally efficient nonlinear filtering algorithms, and a solution to the estimation problems in space situational awareness. Deliverables include documentation of research results, a MATLAB toolbox for sparse grid filtering/space situational awareness, engineering student education and seminars, and student hands-on experience in space object tracking.
If successful, the situational awareness solution will help provide timely and accurate information, data, and services regarding the space/ground environment, and reduce hazards to infrastructure in orbit and on the ground. It will make a great contribution to the safe operation of space and transportation systems and thus have a direct impact on the economy and national security. The sparse-grid based estimation, in a broader perspective, provides a class of accurate and computationally efficient algorithms to solve complex estimation problems for many high dimensional and nonlinear dynamical systems such as multi-agent systems, sensor networks, and power systems. The integration of the research results into the education programs through a variety of activities in curriculum design, student project involvement, and outreach will help recruit and retain minority students and motivate young students to pursue their careers in engineering or science.
