论文标题
加权信息过滤，平滑和序列测量处理
Weighted Information Filtering, Smoothing, and Out-of-Sequence Measurement Processing
论文作者
论文摘要
我们考虑动态系统中的状态估计问题，并提出了处理未模拟系统不确定性的不同机制。我们没有注入随机过程噪声，而是将不同的权重分配给测量值，以便将更多的测量值分配得更多。指数衰减的重量功能的特定选择导致具有与Kalman滤波器基本相同的递归结构的算法。但是，它以旧数据和新数据的方式有所不同。尽管在经典KF中，通过添加过程噪声协方差来使与先前估计值相关的不确定性膨胀，但在本情况下，不确定性通胀是通过将先前的协方差矩阵乘以指数因素来完成的。这种差异使我们能够使用基本相同的算法来解决更多的问题。因此，我们提出了一种统一，最佳的，从最小二乘的意义上讲，用于过滤，预测，平滑和一般序列更新的方法。所有这些任务都需要不同的类似Kalman的算法时，以经典的方式解决。
We consider the problem of state estimation in dynamical systems and propose a different mechanism for handling unmodeled system uncertainties. Instead of injecting random process noise, we assign different weights to measurements so that more recent measurements are assigned more weight. A specific choice of exponentially decaying weight function results in an algorithm with essentially the same recursive structure as the Kalman filter. It differs, however, in the manner in which old and new data are combined. While in the classical KF, the uncertainty associated with the previous estimate is inflated by adding the process noise covariance, in the present case, the uncertainty inflation is done by multiplying the previous covariance matrix by an exponential factor. This difference allows us to solve a larger variety of problems using essentially the same algorithm. We thus propose a unified and optimal, in the least-squares sense, method for filtering, prediction, smoothing and general out-of-sequence updates. All of these tasks require different Kalman-like algorithms when addressed in the classical manner.
