We consider the robust filtering problem for a state-space model with outliers in correlated measurements. We propose a new robust filtering framework to further improve the robustness of conventional robust filters. Specifically, the measurement fitting error is processed separately during the reweighting procedure, which differs from existing solutions where a jointly processed scheme is involved. Simulation results reveal that, under the same setup, the proposed method outperforms the existing robust filter when the outlier-contaminated measurements are correlated, while it has the same performance as the existing one in the presence of uncorrelated measurements since these two types of robust filters are equivalent under such a circumstance.

翻译：我们考虑了在相关测量中带有离子的州空间模型的稳健过滤问题。 我们提出了一个新的稳健过滤框架,以进一步提高常规稳健过滤器的稳健性。 具体地说,测量适配错误在重加权过程中单独处理,这与涉及联合处理计划的现有解决方案不同。 模拟结果表明,在同一设置下,当外部污染测量结果相互关联时,拟议方法优于现有稳健过滤器,而其性能与现有框架相同,因为在这种情况下,这两类稳健过滤器是等效的。