Event-based state estimation

Event-Based State Estimation: Markov Chain Approximation Algorithm (EBMC) [1]

This study presents a general framework for the continuous-time nonlinear event-based state estimation problem.

Using the information from observations made by event-based sampling, the goal of the event-based estimation problem is to estimate the state of stochastic differential equations (SDEs) which represent the uncertain system dynamics.
This problem is challenging because measurements are taken only if some events happen rather than with a ﬁxed sampling interval.

In this study, a theoretical solution for the event-based state estimation problem is derived and a numerical algorithm based on Markov chain approximation is proposed. The proposed algorithm for the event-based state estimation is demonstrated with an illustrative example.

RMS errors of the proposed algorithm (EBMC) and the particle-based algorithm (EBParticle) with 100 Monte Carlo simulations

Event-based State Estimation for Stochastic Hybrid Systems (EBHSE) [2]

This study presents a state estimation algorithm for the stochastic hybrid system (SHS) with event-based sampling.

In event-based sampling, sensors transmit their measurements to an estimator only when predeﬁned events happen, to reduce the communication cost.

On the basis of the event-based sampling, the hybrid state estimation problem is formulated as to compute the probability density of the hybrid state with the sequence of noisy measurements generated at certain events.

This hybrid state estimation problem is challenging since it requires computation of the exponentially increasing number of probabilities of the discrete state histories and evaluation of the multivariate integration.
The proposed event-based hybrid state estimation algorithm utilizes the interacting multiple model (IMM) approach and pseudo measurement generation method to overcome these difﬁculties.

The algorithm is then demonstrated with an illustrative aircraft tracking example.

RMS errors of the proposed algorithm (EBHSE) and the baseline algorithm (IMM) with 100 Monte Carlo simulations

Related Publications

S. Lee, W. Liu, and I. Hwang, "Markov Chain Approximation Algorithm for Event-Based State Estimation," IEEE Transactions on Control Systems Technology, Vol. 23(3), pp. 1123-1130 May 2015
S. Lee and I. Hwang, “Event-Based State Estimation for Stochastic Hybrid Systems”, IET Control Theory and Applications, June 2015, DOI: 10.1049/iet-cta.2014.1205

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