论文标题
在UKF中近似联合状态的Laplacian先验和模型估计
Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF
论文作者
论文摘要
基于模型的观察者在州估计中的一个重大挑战是缺乏相关动态的低质量模型。我们通过同时估计系统状态及其模型不确定性来解决这个问题。具体而言，我们通过线性组合的参数向量扩展了状态，该组合包含近似于缺乏动力学的合适函数。假定只有几个动态术语是相关的，因此参数向量被认为是稀疏的。在贝叶斯环境中，像稀疏之类的属性通过先前的分布表示。稀疏性的一种常见选择是拉普拉斯分布。但是，由于laplacian先验的某些缺点，应用了正规的马蹄分布，即大约具有稀疏性的高斯人。结果显示出较小的估计误差，并通过自动模型还原技术检测到模型改进。
A major challenge in state estimation with model-based observers are low-quality models that lack of relevant dynamics. We address this issue by simultaneously estimating the system's states and its model uncertainties by a square root UKF. Concretely, we extend the state by the parameter vector of a linear combination containing suitable functions that approximate the lacking dynamics. Presuming that only a few dynamical terms are relevant, the parameter vector is claimed to be sparse. In Bayesian setting, properties like sparsity are expressed by a prior distribution. One common choice for sparsity is a Laplace distribution. However, due to some disadvantages of a Laplacian prior, the regularized horseshoe distribution, a Gaussian that approximately features sparsity, is applied. Results exhibit small estimation errors with model improvements detected by an automated model reduction technique.
