论文标题
带有卡尔曼过滤器的实时线性操作员建设和状态估计
Real-time Linear Operator Construction and State Estimation with the Kalman Filter
论文作者
论文摘要
卡尔曼过滤器是估计线性高斯系统状态的最强大工具。另外，使用此方法，可以使用期望最大化算法来估计模型的参数。但是，该算法无法实时起作用。因此，我们提出了一种新方法，该方法可用于实时估算系统的过渡矩阵和状态。提出的方法使用三个想法：在观察空间中进行估计，时间不变的间隔和在线学习框架。应用于阻尼振荡模型，我们获得了非凡的性能来估计矩阵。此外，通过将定位和空间均匀性引入所提出的方法，我们证明可以在高维时空数据中降低噪声。此外，所提出的方法具有在天气预报和矢量场分析等领域的潜力。
The Kalman filter is the most powerful tool for estimation of the states of a linear Gaussian system. In addition, using this method, an expectation maximization algorithm can be used to estimate the parameters of the model. However, this algorithm cannot function in real time. Thus, we propose a new method that can be used to estimate the transition matrices and the states of the system in real time. The proposed method uses three ideas: estimation in an observation space, a time-invariant interval, and an online learning framework. Applied to damped oscillation model, we have obtained extraordinary performance to estimate the matrices. In addition, by introducing localization and spatial uniformity to the proposed method, we have demonstrated that noise can be reduced in high-dimensional spatio-temporal data. Moreover, the proposed method has potential for use in areas such as weather forecasting and vector field analysis.
