Tobit Kalman Filter With Time-Correlated Multiplicative Sensor Noises Under Redundant Channel Transmission

Abstract

Censored measurements arise frequently in engineering practice involving a large number of low-cost off-the-shelf sensors. On the other hand, sensor measurements often suffer from intermittent failures in data transmissions, and an effective way to improve the transmission reliability is to adopt the redundant channel transmission protocol. In this paper, the Tobit Kalman filtering problem is investigated for linear discrete time-varying systems with censored measurements, intermittent failures and time-correlated multiplicative measurement noises under the redundant channel transmission protocol. The Tobit regression model is first modified to take into account the complexities contributed by measurement noises, intermittent failures as well as the redundant channels. Then, an optimal Tobit Kalman filter is designed based on the modified Tobit regression model. In the developed algorithm for the filter design, several new terms are introduced to reflect addressed the complexities, all of which can be calculated recursively or off-line. Simulation results are provided to illustrate the effectiveness of the proposed filter.