论文标题
使用集合Kalman滤波器识别趋势系数
Identifying trending coefficients with an ensemble Kalman filter
论文作者
论文摘要
本文扩展了集合卡尔曼滤波器（ENKF），以识别趋势模型系数。这是通过在保持颗粒的平均值的同时反复夸大合奏来完成的。作为基准提供经典的ENKF和递归最小二乘（RLS），以识别铣削中的力模型的示例，由于工具磨损的进展，这种模型会发生变化。为了进行适当的比较，将模拟真实值并用白色高斯噪声增强。结果表明，在静态情况下仍能达到良好的精度，该方法的动态识别方法的可行性。此外，膨胀的ENKF在起始组上表现出非常不敏感的，但与经典的ENKF相比，收敛较低。
This paper extends the ensemble Kalman filter (EnKF) for inverse problems to identify trending model coefficients. This is done by repeatedly inflating the ensemble while maintaining the mean of the particles. As a benchmark serves a classic EnKF and a recursive least squares (RLS) on the example of identifying a force model in milling, which changes due to the progression of tool wear. For a proper comparison, the true values are simulated and augmented with white Gaussian noise. The results demonstrate the feasibility of the approach for dynamic identification while still achieving good accuracy in the static case. Further, the inflated EnKF shows a remarkably insensitivity on the starting set but a less smooth convergence compared to the classic EnKF.
