On-board spacecraft relative pose estimation with high-order extended Kalman filter

This paper analyzes the real-time relative pose estimation and attitude prediction of a tumbling target spacecraft through a high-order numerical extended Kalman filter based on differential algebra. Indeed, in the differential algebra framework, the Taylor expansion of the phase flow is automatically available once the spacecraft dynamics is integrated and thus the need to write and integrate high-order variational equations is completely avoided making the presented solution easier to implement. To validate the technique, the ESA's e.deorbit mission, involving the Envisat satellite, is used as reference test case. The developed algorithms are implemented on a BeagleBone Black platform, as representative of the limited computational capability available on onboard processors. The performance is assessed by varying the measurement acquisition frequency and processor clock frequency, and considering various levels of uncertainties. A comparison among the different orders of the filter is carried out.