Quadrature-based nonlinear joint probabilistic data association filter

This paper deals with the coupled problem of data association and nonlinear estimation for enhanced Space Situational Awareness (SSA) applications. High-order quadrature methods are used to implement a joint probabilistic data association (JPDA) filter to associate multiple measurements to multiple targets at any given point of time. It is shown that the use of higher-order quadrature methods achieves greater accuracy and stability. In particular, the recently developed conjugate unscented transformation is used to estimate the association probabilities and state estimates. Numerical simulations are used to illustrate the performance of the JPDA filter for SSA applications.