State estimator for multisensor systems with irregular sampling and time-varying delays

Abstract

This article addresses the state estimation in linear time-varying systems with several sensors with different availability, randomly sampled in time and whose measurements have a time-varying delay. The approach is based on a modification of the Kalman filter with the negative-time measurement update strategy, avoiding running back the full standard Kalman filter, the use of full augmented order models or the use of reorganisation techniques, leading to a lower implementation cost algorithm. The update equations are run every time a new measurement is available, independently of the time when it was taken. The approach is useful for networked control systems, systems with long delays and scarce measurements and for out-of-sequence measurements.

Keywords:

• state estimation
• scarce measurements
• time-varying delays

Acknowledgements

This work was supported by project number DPI2008-06731-C02-02/DPI.