In this paper, a novel robust Student’s -based cubature information filter is proposed for a nonlinear multisensor system with heavy-tailed process and measurement noises. At first, the predictive probability density function (PDF) and the likelihood PDF are approximated as two different Student’s distributions. To avoid the process uncertainty induced by the heavy-tailed process noise, the scale matrix of the predictive PDF is modeled as an inverse Wishart distribution and estimated dynamically. Then, the predictive PDF and the likelihood PDF are transformed into a hierarchical Gaussian form to obtain the approximate solution of posterior PDF. Based on the variational Bayesian approximation method, the posterior PDF is approximated iteratively by minimizing the Kullback-Leibler divergence function. Based on the posterior PDF of the auxiliary parameters, the predicted covariance and measurement noise covariance are modified. And then the information matrix and information state are updated by summing the local information contributions, which are computed based on the modified covariance. Finally, the state, scale matrix, and posterior densities are estimated after fixed point iterations. And the simulation results for a target tracking example demonstrate the superiority of the proposed filter.

中文翻译：

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新型鲁棒的基于学生的带有重尾噪声的Cubature信息滤波器

在本文中，一个新颖的鲁棒学生-基于数值积分信息过滤器提出了一种用于具有重尾过程和测量噪声的非线性多传感器系统。首先，将预测概率密度函数（PDF）和似然PDF近似为两个不同的学生分布。为了避免由重尾过程噪声引起的过程不确定性，将预测PDF的比例矩阵建模为Wishart逆分布并动态估算。然后，将预测PDF和似然PDF转换为分层高斯形式，以获得后PDF的近似解。基于变分贝叶斯近似方法，通过最小化Kullback-Leibler发散函数来迭代逼近后PDF。基于辅助参数的后PDF，修改了预测协方差和测量噪声协方差。然后，通过对局部信息贡献求和来更新信息矩阵和信息状态，这些贡献是基于修改后的协方差来计算的。最后，状态，比例矩阵，在定点迭代后估计后密度。目标跟踪实例的仿真结果证明了该滤波器的优越性。