Spacecraft angular rate estimation from vector observations using a fast particle filter

A novel algorithm is presented for the estimation of spacecraft angular-rate from vector observations. Belonging to the class of Monte Carlo sequential methods, the new estimator is a particle filter that uses approximate numerical representation techniques for performing the otherwise exact time propagation and measurement update of potentially non-Gaussian probability density functions (pdf) in inherently nonlinear systems. The paper develops the filter and its implementation in the case of a low Earth orbit (LEO) spacecraft, acquiring noisy Geomagnetic field measurements via a three-axis magnetometer (TAM). The new estimator copes with the absence of an exact inertia tensor by employing a static particle filter which derives a maximum likelihood estimate of the tensor of inertia, thus avoiding the need to expand the filter's state. This renders the new estimator highly efficient and enables its implementation with a remarkably small number of particles. The results of a simulation study are presented, in which the new filter is compared to a conventional extended Kalman filter recently presented in the literature. The comparison demonstrates the viability and robustness of the new algorithm and its fast convergence rate.