DA-based nonlinear filters for spacecraft relative state estimation

Active debris removal (ADR) missions have gained increasing importance in the space community due to the necessity of reducing the number of debris jeopardizing the operative satellites. In this context, autonomous guidance, navigation and control (GNC) plays a fundamental role in the problem of rendezvous with an uncooperative target. Especially, the estimation of the relative pose and the prediction of the target attitude are crucial for safe proximity operations. Therefore, a key point for the success of ADR missions is the development of efficient algorithms capable of limiting the computational burden without losing out the necessary performance. To this aim, this study analyzes the exploitation of nonlinear filters based on differential algebra (DA). Especially, high-order numerical extended Kalman filter and unscented Kalman filter are implemented in the DA framework. The ESA’s e.deorbit mission, involving Envisat satellite, is used as reference test case. Both filters are applied to this target application and compared in terms of accuracy and computational burden.