State estimation is a fundamental task in many engineering fields, and therefore robust nonlinear filtering techniques able to cope with misspecified, uncertain and/or corrupted models must be designed for real-life applicability. In this contribution we explore nonlinear Gaussian filtering problems where measurements may be corrupted by outliers, and propose a new robust variational-based filtering methodology able to detect and mitigate their impact. This method generalizes previous contributions to the case of multiple outlier indicators for both independent and dependent observation models. An illustrative example is provided to support the discussion and show the performance improvement.

中文翻译：

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稳健的基于变分的卡尔曼滤波器，用于通过相关测量进行异常值抑制

状态估计是许多工程领域的一项基本任务，因此必须设计能够处理错误指定、不确定和/或损坏的模型的鲁棒非线性滤波技术，以便在现实生活中适用。在这篇文章中，我们探索了非线性高斯滤波问题，其中测量值可能被异常值破坏，并提出了一种新的基于变分的稳健滤波方法，能够检测和减轻它们的影响。该方法概括了先前对独立和相关观察模型的多个异常值指标情况的贡献。提供了一个说明性示例以支持讨论并显示性能改进。