Global Estimation Method Based on Spatial-Temporal Kalman Filter for DPOS
Global Estimation Method Based on Spatial-Temporal Kalman Filter for DPOS
复制标题
基于时空卡尔曼滤波器的DPOS全局估计方法
DOI：
10.1109/jsen.2020.3027582
复制
发表时间：
2021-02-01
影响因子：
4.3
通讯作者：
Gu, Bin
中科院分区：
文献类型：
--
作者：
Liu, Yanhong;Wang, Bo;Gu, Bin
分布式定位定向系统（DPOS）可以为多任务遥感载荷提供丰富的运动参数，进行运动补偿。通过主系统到从惯性测量单元（伊穆斯）的传递对准，可以获得带有从惯导系统的成像负载的运动参数。然而，考虑到从IMU的体积、重量和成本，在工程实践中不可能在所有遥感负载附近安装伊穆斯。针对这一问题，提出了一种基于时空卡尔曼滤波（STKF）的DPOS全局估计方法，用于估计未配备从伊穆斯的遥感负载的运动参数。时空卡尔曼滤波结合克里金法和卡尔曼滤波，考虑到点与点之间的时空相关性，可以充分利用数据的有用信息，使我们在时间和空间上都能得到最优估计。为了验证所提方法的有效性，进行了基于飞行试验的半物理仿真。结果表明，该方法不仅可以实现全局估计，而且为DPOS布局方案的优化提供了新的思路。
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.