Application of Correntropy-Based CDKF in Transfer Alignment for Accuracy Enhancement of Airborne Distributed POS
Application of Correntropy-Based CDKF in Transfer Alignment for Accuracy Enhancement of Airborne Distributed POS
复制标题
基于互熵的CDKF在提高机载分布式POS传输对准精度中的应用
DOI：
10.1109/jsen.2021.3129605
复制
发表时间：
2022-01
影响因子：
4.3
通讯作者：
王博
中科院分区：
文献类型：
--
作者：
刘艳红;叶文;王博
对于分布式定位定向系统（POS），多节点运动信息的测量主要采用传递对准技术。基于KF的非线性滤波算法为传递对准提供了最优的综合解。然而，由于外部环境的不确定性和柔性杆臂补偿误差，传递对准的噪声呈现非高斯特性，从而导致分布式POS的精度较低。本文将中心差分卡尔曼滤波器（CDKF）与最大相关熵准则（MCC）相结合，提出了一种基于相关性的CDKF，以降低非高斯噪声的影响。通过重构线性回归模型，定义MCC下的代价函数来修正量测信息，最终实现精确的状态估计。同时，建立了考虑柔性变形和杠杆臂效应的非线性传递对准误差模型，并将主POS的姿态和速度信息传递给从IMU，以提高其性能。半物理仿真结果表明，该方法的估计精度优于标准CDKF。
For distributed Position and Orientation System (POS), multi-node motion information are measured mainly by transfer alignment technology. The KF-based non-linear filtering algorithm provides optimal integrated solution for transfer alignment. However, on account of uncertain external environment and flexible lever arm compensation error, the noises of transfer alignment exhibit non-Gaussian property and further cause lower accuracy of distributed POS. In this paper, Correntropy-based CDKF is proposed by combining central difference Kalman filter (CDKF) and maximum correntropy criterion (MCC), which aims to decrease the effect of non-Gaussian noises. We rebuild a linear regression model and define a cost function under MCC to modify measurement information, and finally accurate state estimation is achieved. Meanwhile, the error model of nonlinear transfer alignment is formulated where flexible deformation and lever arm effect are considered, and the attitude and velocity information of main POS are transferred to slave IMU to improve its performance. Based on a semi-physical simulation, the performance of proposed method is proved to outperform the standard CDKF in term of estimation accuracy.