Global Estimation Method Based on Spatial–Temporal Kalman Filter for DPOS

A distributed position and orientation system (DPOS) can provide abundant motion parameters for multi-task remote sensing loads to conduct its motion compensation. The motion parameters of imaging loads equipped with slave Inertial Measurement Units (IMUs) can be obtained by transfer alignment from master system to slave IMU. However, considering the volume, weight, and cost of the slave IMU, it is impossible to install IMUs near all the remote sensing loads in engineering practice. Aiming at the problem, a global estimation method based on spatial-temporal Kalman filter (STKF) for DPOS is proposed to estimate the motion parameters of remote sensing loads not equipped with slave IMUs. Combining kriging and Kalman filter, and taking into account the spatial-temporal correlation between points, spatial-temporal Kalman filter can make full use of the useful information of the data and enable us to obtain the optimal estimation in time and space. In order to evaluate the effectiveness of the proposed method, the semi-physical simulation based on flight experiment is conducted. The results show that the proposed method not only can realize the global estimation, but also provides us some new insights into the layout scheme of DPOS.