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.

本文涉及过程测量值被审查时的卡尔曼滤波。审查测量由 I 型托比特模型解决，并且是具有两个审查极限的一维，而（隐藏）状态向量是多维的。对于该模型，通过卡尔曼滤波类型的递归算法提供状态向量的贝叶斯估计。实验验证了该算法的有效性和适用性。实验表明，所提出的方法在最小化计算成本以及合成和真实数据集的整体均方根误差（RMSE）方面优于其他过滤方法。