Kalman filtering with censored measurements

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.

Automated summary

This paper discusses the use of Kalman filtering in the presence of censored process measurements, utilizing a Tobit Type I model to handle the censored data. Bayesian estimates for multidimensional state vectors are provided through a recursive Kalman filtering algorithm. Experimental results demonstrate that the proposed method effectively reduces computational costs and improves overall accuracy when compared to other filtering methodologies for synthetic and real data sets.