In this contribution, we introduce a generalized Kalman filter with precision in recursive form when the stochastic model is misspecified. The filter allows for a relaxed dynamic model in which not all state vector elements are connected in time. The filter is equipped with a recursion of the actual error-variance matrices so as to provide an easy-to-use tool for the efficient and rigorous precision analysis of the filter in case the underlying stochastic model is misspecified. Different mechanizations of the filter are presented, including a generalization of the concept of predicted residuals as needed for the recursive quality control of the filter.

中文翻译：

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当随机模型被错误指定时，具有递归形式的精度的广义卡尔曼滤波器

在这个贡献中，当随机模型被错误指定时，我们以递归形式引入了一个具有精度的广义卡尔曼滤波器。过滤器允许松弛的动态模型，其中并非所有状态向量元素都及时连接。该过滤器配备了实际误差方差矩阵的递归，以便在错误指定基础随机模型的情况下为过滤器的高效和严格精度分析提供一种易于使用的工具。介绍了过滤器的不同机制，包括过滤器递归质量控制所需的预测残差概念的概括。