基于自然梯度的噪声自适应变分贝叶斯滤波算法

Considering the increasing complexity and changeability of characteristics such as maneuvering and stealth in moving target tracking system and the influence of adverse factors such as non-line-of-sight, interference and occlusion in measurement environment. State estimation is likely to be confronted with complex system characteristics such as nonlinearity, non-Gaussian noise and unknown parameters. Aiming at nonlinear adaptive state estimation of moving target in a system with unknown process noise and non-Gaussian measurement noise, a novel noise adaptive variational Bayesian (VB) filter using natural gradient is proposed. Firstly, a parameterized inverseWishart (IW) distribution and a student＇s t distribution are constructed as the conjugate prior distribution of predicted state error covariance and measurement likelihood respectively. Then, in the framework of variational Bayesian optimization, the joint a posteriori distribution of estimation variables is approximately decomposed into independent variational distributions by using mean-field theory. On this basis, the variational distribution parameters of each variable are updated by combining coordinate ascend method and the characteristics of exponential distributions. Furthermore, under the condition of maximizing evidence lower bound, the natural gradients with respect to state estimation and its error covariance are derived by combining with Fisher information matrix. So that the variational distribution of nonlinear state gradually approaches the posteriori probability density function (PDF) of state along the natural gradient direction. Finally, simulation results show that the proposed algorithm has better adaptive ability to the problem of noise uncertainty and can obtain higher estimation accuracy compared to traditional algorithms.