Particle filter based tracking in a detection sparse discrete event simulation environment
One of the key abilities of agents in military simulations is to react to both detections of and counter-detections by other agents in the environment. While methods have been developed to model these detections and counter-detections, the majority of these methods model detection and counter-det...
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Monterey, California. Naval Postgraduate School
2012
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ndltd-nps.edu-oai-calhoun.nps.edu-10945-36482014-11-27T16:04:47Z Particle filter based tracking in a detection sparse discrete event simulation environment Borovies, Drew A. Darken, Christian Buss, Arnold. Naval Postgraduate School Modeling, Virtual Environments and Simulation (MOVES) One of the key abilities of agents in military simulations is to react to both detections of and counter-detections by other agents in the environment. While methods have been developed to model these detections and counter-detections, the majority of these methods model detection and counter-detection as an all or nothing prospect in which an un-detected entity at some point crosses an arbitrary threshold of observability and becomes fully detected. In actuality, even extremely uncertain or incomplete detections and counter-detections of opposing entities can provide enough data for entities to make reasonably intelligent decisions on the virtual battlefield. Recent developments in commercial gaming artificial intelligence suggest that particle-based tracking techniques can provide accurate and computationally efficient state estimation of opposing agents within virtual environments. In this work several particle-based methods for obtaining and tracking contacts are explored to determine the feasibility of their use as a general purpose tracking technique in military simulations. 2012-03-14T17:38:58Z 2012-03-14T17:38:58Z 2007-03 Thesis http://hdl.handle.net/10945/3648 123960581 Approved for public release, distribution unlimited Monterey, California. Naval Postgraduate School |
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description |
One of the key abilities of agents in military simulations is to react to both detections of and counter-detections by other agents in the environment. While methods have been developed to model these detections and counter-detections, the majority of these methods model detection and counter-detection as an all or nothing prospect in which an un-detected entity at some point crosses an arbitrary threshold of observability and becomes fully detected. In actuality, even extremely uncertain or incomplete detections and counter-detections of opposing entities can provide enough data for entities to make reasonably intelligent decisions on the virtual battlefield. Recent developments in commercial gaming artificial intelligence suggest that particle-based tracking techniques can provide accurate and computationally efficient state estimation of opposing agents within virtual environments. In this work several particle-based methods for obtaining and tracking contacts are explored to determine the feasibility of their use as a general purpose tracking technique in military simulations. |
author2 |
Darken, Christian |
author_facet |
Darken, Christian Borovies, Drew A. |
author |
Borovies, Drew A. |
spellingShingle |
Borovies, Drew A. Particle filter based tracking in a detection sparse discrete event simulation environment |
author_sort |
Borovies, Drew A. |
title |
Particle filter based tracking in a detection sparse discrete event simulation environment |
title_short |
Particle filter based tracking in a detection sparse discrete event simulation environment |
title_full |
Particle filter based tracking in a detection sparse discrete event simulation environment |
title_fullStr |
Particle filter based tracking in a detection sparse discrete event simulation environment |
title_full_unstemmed |
Particle filter based tracking in a detection sparse discrete event simulation environment |
title_sort |
particle filter based tracking in a detection sparse discrete event simulation environment |
publisher |
Monterey, California. Naval Postgraduate School |
publishDate |
2012 |
url |
http://hdl.handle.net/10945/3648 |
work_keys_str_mv |
AT boroviesdrewa particlefilterbasedtrackinginadetectionsparsediscreteeventsimulationenvironment |
_version_ |
1716720816979181568 |