Event-Based State Estimation Using the Auxiliary Particle Filter

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Event-based sampling provides a way of lowering the resource utilization in sensing and communication applications. By sending a sample only when some triggering condition is fulfilled, we can ensure that the transmitted samples actually carry innovation. However, in an event-based system, the state estimation problem becomes complicated, as the information of not receiving a measurement must be taken into consideration. Recent research has examined the feasibility of using particle filters for solving the event-based state estimation problem. To the best of our knowledge, only the simple bootstrap particle filter has so far been considered in this setting. We argue that, as this filter does not fully utilize the current measurement, it is not well suited for state estimation in event-based systems.
We propose an extension to the auxiliary particle filter for systems with event-based measurements, in which certain existing techniques for finding an approximation of the fully adapted filter can easily be utilized. In a simulation study, we demonstrate that at new measurement events, the benefits of using the auxiliary particle filter increases when fewer measurements are being sent.
Original languageEnglish
Title of host publication2019 European Control Conference (ECC)
PublisherIEEE Press
ISBN (Electronic)978-3-907144-00-8
Publication statusPublished - 2019
Event2019 European Control Conference - Napoli, Italy
Duration: 2019 Jun 252019 Jun 28


Conference2019 European Control Conference
Abbreviated titleECC19
Internet address

Subject classification (UKÄ)

  • Control Engineering


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