Differential Algebra-Based Multiple Gaussian Particle Filter for Orbit Determination

The nonlinear filtering problem plays a fundamental role in multiple space-related applications. This paper offers a new filtering technique that combines Monte Carlo time propagation with a Gaussian mixture model measurement update. Differential algebra (DA) techniques are used as a tool to reduce the computational effort required by particle filters. Moreover, the use of expectation maximization (EM) optimization algorithm leads to a good approximation of the probability density functions. The performance of the new method is assessed in the nonlinear orbit determination problem, for the challenging case of low observations frequency, and in the restricted three-body dynamics.