论文标题
在模型扰动下稳健的固定滞后平滑
Robust fixed-lag smoothing under model perturbations
论文作者
论文摘要
在这种情况下，提出了一种稳健的固定滞后平滑方法，标称模型与实际模型之间存在不匹配。由此产生的稳健更光滑的特征是两个玩家之间的动态游戏：一个玩家在规定的歧义集中选择最不利的模型，而另一个玩家选择了固定落叶的固定粘贴更加平滑级别，以最大程度地减少相对于最不利的模型的平滑误差。我们提出了有效的拟议更光滑的实施。此外，我们在有限的时间范围内表征了相应的最小值模型。最后，我们在两个示例中测试了稳健的固定滞后更平滑。第一个涉及目标跟踪问题，而第二个则为参数估计问题。
A robust fixed-lag smoothing approach is proposed in the case there is a mismatch between the nominal model and the actual model. The resulting robust smoother is characterized by a dynamic game between two players: one player selects the least favorable model in a prescribed ambiguity set, while the other player selects the fixed-lag smoother minimizing the smoothing error with respect to least favorable model. We propose an efficient implementation of the proposed smoother. Moreover, we characterize the corresponding least favorable model over a finite time horizon. Finally, we test the robust fixed-lag smoother in two examples. The first one regards a target tracking problem, while the second one regards a parameter estimation problem.
