论文标题
在模型不确定性下的低排名卡尔曼过滤
Low-rank Kalman filtering under model uncertainty
论文作者
论文摘要
我们考虑了一个可靠的过滤问题，其中名义状态空间模型无法达到与实际情况不同。我们提出了一个强大的Kalman滤波器，该过滤器可以解决动态游戏：一个玩家在给定的歧义集中选择最不利的模型，而另一个玩家则设计了最佳模型的最佳滤镜。事实证明，稳健的滤波器由低级别风险敏感的riccati方程控制。最后，仿真结果显示了提出的滤波器的有效性。
We consider a robust filtering problem where the nominal state space model is not reachable and different from the actual one. We propose a robust Kalman filter which solves a dynamic game: one player selects the least-favorable model in a given ambiguity set, while the other player designs the optimum filter for the least-favorable model. It turns out that the robust filter is governed by a low-rank risk sensitive-like Riccati equation. Finally, simulation results show the effectiveness of the proposed filter.
