论文标题
促进UKF的稀疏性共同估算状态和模型不确定性
Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF
论文作者
论文摘要
在仅提供考虑的系统的部分模型时，国家估计仍然是许多工程领域的主要挑战。这项工作提出了一个联合平方根的无意义的卡尔曼过滤器，以通过物理动机库函数的线性组合同时估算状态并模拟不确定性。使用稀疏性促进方法，选择了这些线性组合的选择，因此可以提取可解释的模型。结果表明，与传统的平方根无声的卡尔曼过滤器相比，估计误差很小，并且表现出了物理上有意义的模型的增强。
State estimation when only a partial model of a considered system is available remains a major challenge in many engineering fields. This work proposes a joint, square-root unscented Kalman filter to estimate states and model uncertainties simultaneously by linear combinations of physics-motivated library functions. Using a sparsity promoting approach, a selection of those linear combinations is chosen and thus an interpretable model can be extracted. Results indicate a small estimation error compared to a traditional square-root unscented Kalman filter and exhibit the enhancement of physically meaningful models.
