非高斯环境下船变测量最大熵卡尔曼滤波方法

在进行惯性量匹配船体变形测量时, 光纤陀螺组件(Fiber Gyroscope Unit, FGU)往往暴露在复杂海况的外扰环境中, 经典卡尔曼滤波误差较大甚至出现发散。针对于此, 引入最大熵准则(Maximum Correntropy Criterion, MCC)和基于权重矩阵的加权最小二乘(Weighted Least Squares, WLS)方法改进KF迭代过程, 推导了一种用于噪声模型未知和非高斯环境中的MCC卡尔曼滤波器(MCC Kalman filter, MCCKF)。最后, 基于角速度匹配法开展多组对比实验。实验证实相较于自适应和传统MCC滤波器, 新算法无需实时调参, 抑制外扰能力更优, 可实现更高精度的变形角估计, 满足快速收敛和抗复杂外扰的鲁棒性要求。

Maximum correntropy Kalman filter used for hull deformation measurement in non-Gaussian environment

In the process of hull deformation measurement based on inertial matching method, the conventional Kalman filter possibly causes a greater deviation or even divergence of filtering when the fiber gyroscope unit (FGU) is being exposed to a certain external environment under complex sea conditions. To solve this problem, this paper introduces a maximum correntropy criterion Kalman filter (MCCKF) used for environment under model-unknown or non-Gaussian noises based on weighted matrix, into which both the maximum correntropy criterion (MCC) and weighted least squares (WLS) are introduced to improve its iterative process. Finally, a series of comparative experiments based on angular velocity matching method are carried out. Compared with adaptive and traditional MCC Kalman filter (MCCKF) avoids timely modifying parameters and achieves more precise estimation of deformation, meets the requirements of rapid convergence, as well as stronger robustness of resisting complex disturbance.