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Robust estimation using context-aware filtering
Abstract
© 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.
Type
ConferencePermalink
https://hdl.handle.net/10161/11288Published Version (Please cite this version)
10.1109/ALLERTON.2015.7447058Collections
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Show full item recordScholars@Duke
Miroslav Pajic
Dickinson Family Associate Professor
Miroslav Pajic's research focuses on design and analysis of cyber-physical systems
with varying levels of autonomy and human interaction, at the intersection of (more
traditional) areas of embedded systems, AI, learning and controls, formal methods
and robotics.

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