论文标题
通过审查测量值进行的卡尔曼过滤
Kalman Filtering With Censored Measurements
论文作者
论文摘要
本文涉及在审查过程的测量结果时进行的Kalman过滤。审查的测量由I型的TOBIT模型解决，并且是一维的，具有两个审查限制，而（隐藏的）状态向量是多维的。对于此模型，通过卡尔曼过滤类型的递归算法提供了对状态向量的贝叶斯估计。提出了实验以说明算法的有效性和适用性。实验表明，该方法在最小化计算成本以及合成和真实数据集的总体均值误差（RMSE）方面优于其他过滤方法。
This paper concerns Kalman filtering when the measurements of the process are censored. The censored measurements are addressed by the Tobit model of Type I and are one-dimensional with two censoring limits, while the (hidden) state vectors are multidimensional. For this model, Bayesian estimates for the state vectors are provided through a recursive algorithm of Kalman filtering type. Experiments are presented to illustrate the effectiveness and applicability of the algorithm. The experiments show that the proposed method outperforms other filtering methodologies in minimizing the computational cost as well as the overall Root Mean Square Error (RMSE) for synthetic and real data sets.
