Adaptive Particle Filtering for Spacecraft Attitude Estimation from Vector Observations

Abstract

An extension is presented to the recently introduced genetic algorithm-embedded quaternion particle filter. Belonging to the class ofMonte Carlo sequential methods, the genetic algorithm-embedded quaternion particle filter is an estimator that uses approximate numerical representation techniques for performing the otherwise exact time propagation and measurement update of potentially non-Gaussian probability density functions in the inherently nonlinear attitude estimation problem. The spacecraft attitude is represented via the quaternion of rotation, and a genetic algorithm is used to estimate the gyrobiases, allowing one to estimate just the quaternion via the particlefilter. An adaptive version of the genetic algorithm-embedded quaternion particle filter is presented herein that extends the applicability of this filter to problems with highly uncertain measurement noise distributions. The adaptive algorithm estimates themeasurement noise distribution on the fly, alongwith the spacecraft attitude and gyro biases. A simulation study is used to demonstrate the performance of the adaptive algorithm using real data obtained from the Technion’s TechSAT satellite, whose three-axis magnetometer’s data are non-Gaussian. The simulation, which compares the performance of the filter to the nonadaptive genetic algorithm-embedded quaternion particle filter, demonstrates the viability of the new algorithm.