Robust estimation using context-aware filtering

Published

Conference Paper

© 2015 IEEE. This paper presents the context-aware filter, an estimation technique that incorporates context measurements, in addition to the regular continuous measurements. Context measurements provide binary information about the system's context which is not directly encoded in the state; examples include a robot detecting a nearby building using image processing or a medical device alarming that a vital sign has exceeded a predefined threshold. These measurements can only be received from certain states and can therefore be modeled as a function of the system's current state. We focus on two classes of functions describing the probability of context detection given the current state; these functions capture a wide variety of detections that may occur in practice. We derive the corresponding context-aware filters, a Gaussian Mixture filter and another closed-form filter with a posterior distribution whose moments are derived in the paper. Finally, we evaluate the performance of both classes of functions through simulation of an unmanned ground vehicle.

Full Text

Duke Authors

Cited Authors

  • Ivanov, R; Atanasov, N; Pajic, M; Pappas, G; Lee, I

Published Date

  • April 4, 2016

Published In

  • 2015 53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015

Start / End Page

  • 590 - 597

International Standard Book Number 13 (ISBN-13)

  • 9781509018239

Digital Object Identifier (DOI)

  • 10.1109/ALLERTON.2015.7447058

Citation Source

  • Scopus