Motion and Parameter Estimation of Space Objects Using Laser-Vision Data

A computationally efficient noise-adaptive Kalman filter is presented for the motion estimation and prediction of a free-failing tumbling satellite (target). The filter receives only noisy pose measurements from a laser-vision system aboard another satellite (chaser) at a close distance in a neighboring orbit. The filter estimates the full states, all the inertia parameters of the target satellite, and the covariance of the measurement noise. A comprehensive dynamics model that includes aspects of orbital mechanics is incorporated for accurate estimation. The discrete-time model, which involves a state-transition matrix and the covariance of process noise, is derived in closed form, thus rendering the filter suitable for real-time implementation. The statistical characteristics of the measurement noise is formulated by a state-dependent covariance matrix. This model allows additive quaternion noise, while preserving the unit-norm property of the quaternion. The convergence properties of the developed filter is demonstrated by simulation and experimental results. These results also demonstrate that the filter can continuously produce accurate estimates of pose even when the vision system is occluded for tens of seconds.