Shan Chenghao, Zhou Weidong, Yang Yefeng, Jiang Zihao
College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China.
Center for Control Theory and Guidance Technology, Harbin Institute of Technology, Harbin 150001, China.
Sensors (Basel). 2020 Dec 30;21(1):198. doi: 10.3390/s21010198.
Aiming at the problem that the performance of adaptive Kalman filter estimation will be affected when the statistical characteristics of the process and measurement of the noise matrices are inaccurate and time-varying in the linear Gaussian state-space model, an algorithm of multi-fading factor and an updated monitoring strategy adaptive Kalman filter-based variational Bayesian is proposed. Inverse Wishart distribution is selected as the measurement noise model and the system state vector and measurement noise covariance matrix are estimated with the variational Bayesian method. The process noise covariance matrix is estimated by the maximum a posteriori principle, and the updated monitoring strategy with adjustment factors is used to maintain the positive semi-definite of the updated matrix. The above optimal estimation results are introduced as time-varying parameters into the multiple fading factors to improve the estimation accuracy of the one-step state predicted covariance matrix. The application of the proposed algorithm in target tracking is simulated. The results show that compared with the current filters, the proposed filtering algorithm has better accuracy and convergence performance, and realizes the simultaneous estimation of inaccurate time-varying process and measurement noise covariance matrices.
针对线性高斯状态空间模型中噪声矩阵的过程和测量统计特性不准确且时变时自适应卡尔曼滤波器估计性能会受到影响的问题，提出了一种基于变分贝叶斯的多衰落因子及更新监测策略自适应卡尔曼滤波算法。选择逆 Wishart 分布作为测量噪声模型，采用变分贝叶斯方法估计系统状态向量和测量噪声协方差矩阵。通过最大后验原理估计过程噪声协方差矩阵，并使用带有调整因子的更新监测策略来保持更新矩阵的半正定。将上述最优估计结果作为时变参数引入到多个衰落因子中，以提高一步状态预测协方差矩阵的估计精度。对所提算法在目标跟踪中的应用进行了仿真。结果表明，与当前滤波器相比，所提滤波算法具有更好的精度和收敛性能，实现了对不准确时变过程和测量噪声协方差矩阵的同时估计。
