论文标题
在存在相关测量的情况下，异常可比的卡尔曼过滤器
Outlier-robust Kalman Filter in the Presence of Correlated Measurements
论文作者
论文摘要
我们考虑具有相关测量值的状态空间模型的强大过滤问题。我们提出了一个新的强大过滤框架，以进一步提高常规稳健过滤器的鲁棒性。具体而言，测量拟合误差是在重新加权过程中分别处理的，这与涉及共同处理方案的现有解决方案不同。仿真结果表明，在相同的设置下，当相关的离群值测量值相关时，所提出的方法优于现有的可靠过滤器，而在存在不相关的测量值的情况下，它的性能与现有的性能相同，因为这两种类型的鲁棒过滤器在这种情况下是等效的。
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.
