Abstract
A novel algorithm for efficient high-accuracy satellite attitude estimation is presented to address the increasing performance requirements of resource-constrained small satellites. The algorithm results from an investigation of the Bayesian nonlinear estimation problem based on the phase-space geometry of Hamiltonian systems. Probability density functions are shown to be conserved properties of deterministic Hamiltonian systems, and appropriate geometric integrators exactly preserve the functions as they evolve in time. Based on these insights, a new iterative filter is derived that conserves the geometric structure and invariant properties of the dynamics and exactly preserves the nonlinear a posteriori probability density function when solving for the state estimate. Comparisons with a benchmark iterative filter demonstrate significantly reduced computational burden, improved state estimation accuracy, and improved constants of motion estimation, particularly in the presence of high nonlinearity and low noise inputs. Based on numerous simulations, the authors conclude that this new method shows promise for improved attitude estimation onboard high-performance, resource-constrained small satellites.
Published Version
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