In this letter, a robust minimum error entropy based cubature information filter is proposed for state estimation in non-Gaussian measurement noise. A new combined optimization cost is defined based on the error entropy. Through cubature transform, a statistical linearization regression model is constructed, and a new information filter is then developed by minimizing the error entropy based cost. The fixed-point iteration approach is used to compute the state estimate. Further, the convergence of the proposed information filter is analyzed, and the convergence conditions are derived. Simulations are performed to demonstrate the effectiveness of the proposed algorithm. It is shown that the estimation performance of the proposed filter is more robust than that of traditional methods against the complicated non-Gaussian noises, such as outliers and noises from multimodal distributions.

中文翻译：

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具有非高斯测量噪声的鲁棒最小误差熵的广域信息滤波器

在这封信中，提出了一种基于鲁棒最小误差熵的培养皿信息滤波器，用于非高斯测量噪声中的状态估计。基于误差熵定义了新的组合优化成本。通过孵化变换，构建了统计线性化回归模型，然后通过最小化基于误差熵的成本来开发新的信息过滤器。定点迭代方法用于计算状态估计。此外，分析了所提出的信息滤波器的收敛性，并得出了收敛条件。仿真表明了该算法的有效性。结果表明，针对复杂的非高斯噪声，所提出的滤波器的估计性能比传统方法更健壮，