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

Abstract

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.

Uncooperative Spacecraft Relative Navigation With LIDAR-Based Unscented Kalman Filter

“…Specifically, two separate filters, i.e., an H∞ filter and a second-order, minimum-energy filter on the Lie group, are exploited for the relative orbital and rotational dynamics, respectively. Finally, a high-order EKF exploiting simulated pose estimate from a visual system is proposed in [22].…”

“…where µ is the Earth's gravitational constant, r C is the norm of the position vector of the chaser spacecraft, ω H andω H are the angular velocity and acceleration of HRF in IRF, respectively, while x, y and z are the Cartesian components of the relative position vector ρ. The relative attitude model is represented by (21) which describes the kinematic evolution in time of the relative attitude quaternion [36], while the absolute rotational dynamics of the target is described by the well-known Euler equation (22) under the free-body assumption (i.e., disturbance torques are not included in the model).…”

“…In (21), ω T /C or two different values e chaser with respect to the target in TRF and the symbol ⊗ indicates a quaternion product; in (22), I T is the inertia matrix of the target, assumed to be known.…”

Hybrid extended‐cubature Kalman filters for non‐linear continuous‐time fractional‐order systems involving uncorrelated and correlated noises using fractional‐order average derivative

“…The UKF uses the unscented transformation to deal with the non‐linear functions in the estimated system and achieves the higher accuracy of state estimation than EKF. However, the disadvantages of this method are higher computational complexity and longer computational time [32]. Meanwhile, CKF can achieve an effective state estimation for a non‐linear system.…”

Jet transport-based nonlinear state and parameter estimation for geostationary spacecraft

“…Currently, various filtering algorithms have been proposed and extensively exploited in the IOD problems [3][4][5][6][7]. A complete discussion of the pros and cons of these filtering algorithms can be found in [3,8].…”