Event Based State Estimation With Time Synchronous Updates

• Published in: IEEE transactions on automatic control Vol. 57; no. 10; pp. 2650 - 2655
• Main Authors: Sijs, J., Lazar, M.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-10-2012; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Approximation methods; Asymptotic properties; Computer science; control theory; systems; Control theory. Systems; Data transfer (computers); Estimators; Exact sciences and technology; Gaussian; Kalman filters; Mathematical models; Modelling and identification; Sampling; Stability analysis; State estimation; Studies; Synchronous; Time measurement; Vectors; Bounded error; Measurement sensor; Data transmission; Covariance matrix; Communication network; Computational complexity; Reactive system; Gaussian process; Observer; Asymptotic approximation; System identification; Sampling; State estimation
• ISSN: 0018-9286; 1558-2523
• DOI: 10.1109/TAC.2012.2190197

To reduce the amount of data transfer in networked systems, measurements are usually taken only when an event occurs rather than at each synchronous sample instant. However, this complicates estimation problems considerably, especially in the situation when no measurement is received anymore. The goal of this paper is therefore to develop a state estimator that can successfully cope with event based measurements and attains an asymptotically bounded error-covariance matrix. To that extent, a general mathematical description of event sampling is proposed. This description is used to set up a state estimator with a hybrid update, i.e., when an event occurs the estimated state is updated using the measurement, while at synchronous instants the update is based on knowledge that the sensor value lies within a bounded subset of the measurement space. Furthermore, to minimize computational complexity of the estimator, the algorithm is implemented using a sum of Gaussians approach. The benefits of this implementation are demonstrated by an illustrative example of state estimation with event sampling.