Nonparametric Representations for Integrated Inference, Control, and Sensing
The objective of this research program was to develop mathematical foundations of information gathering through an integrated theory of sensing, inference, and control. The goal of the team was to develop a new framework for autonomous operations that will extend the state of the art in distributed...
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creator | Fisher, John Darrell, Trevor Galup, Luis How, Jon Krause, Andreas Soatto, Stefano |
description | The objective of this research program was to develop mathematical foundations of information gathering through an integrated theory of sensing, inference, and control. The goal of the team was to develop a new framework for autonomous operations that will extend the state of the art in distributed learning and modeling from data, and tightly integrate these models into new decentralized cooperative planning algorithms. The main output of this effort will be a fundamental theory to integrate decentralized information driven planning methods for heterogenous teams with nonparametric Bayesian models of uncertainty. The feasibility and aspects of the value of the theory were demonstrated via integrated software and hardware experiments.
Prepared in cooperation with University of California - Los Angeles (UCLA), ETH Zurich, BAE Systems, and International Computer Science Institute (ICSI). Sponsored in part by DARPA. |
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Prepared in cooperation with University of California - Los Angeles (UCLA), ETH Zurich, BAE Systems, and International Computer Science Institute (ICSI). Sponsored in part by DARPA.</description><language>eng</language><subject>COMPUTER LOGIC ; Cybernetics ; DATA FUSION ; DISTRIBUTED LEARNING ; INFERENCE ; INFORMATION THEORY ; MSEE PROGRAM ; Operations Research ; PE61101E ; QUERY ACCURACY ; RECOGNITION ; SCENE MODELING ; SENSING GEOMETRY ; THREE DIMENSIONAL ; TRACKING ; VIDEO BASED OBJECT TRACKING ; VIDEO IMAGES ; WUAFRL100011Y015</subject><creationdate>2015</creationdate><rights>Approved for public release; distribution is unlimited.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,776,881,27544,27545</link.rule.ids><linktorsrc>$$Uhttps://apps.dtic.mil/sti/citations/ADA627279$$EView_record_in_DTIC$$FView_record_in_$$GDTIC$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Fisher, John</creatorcontrib><creatorcontrib>Darrell, Trevor</creatorcontrib><creatorcontrib>Galup, Luis</creatorcontrib><creatorcontrib>How, Jon</creatorcontrib><creatorcontrib>Krause, Andreas</creatorcontrib><creatorcontrib>Soatto, Stefano</creatorcontrib><creatorcontrib>MASSACHUSETTS INST OF TECH CAMBRIDGE COMPUTER SCIENCE AND ARTIFICIAL INTELLIGENCE LAB</creatorcontrib><title>Nonparametric Representations for Integrated Inference, Control, and Sensing</title><description>The objective of this research program was to develop mathematical foundations of information gathering through an integrated theory of sensing, inference, and control. The goal of the team was to develop a new framework for autonomous operations that will extend the state of the art in distributed learning and modeling from data, and tightly integrate these models into new decentralized cooperative planning algorithms. The main output of this effort will be a fundamental theory to integrate decentralized information driven planning methods for heterogenous teams with nonparametric Bayesian models of uncertainty. The feasibility and aspects of the value of the theory were demonstrated via integrated software and hardware experiments.
Prepared in cooperation with University of California - Los Angeles (UCLA), ETH Zurich, BAE Systems, and International Computer Science Institute (ICSI). Sponsored in part by DARPA.</description><subject>COMPUTER LOGIC</subject><subject>Cybernetics</subject><subject>DATA FUSION</subject><subject>DISTRIBUTED LEARNING</subject><subject>INFERENCE</subject><subject>INFORMATION THEORY</subject><subject>MSEE PROGRAM</subject><subject>Operations Research</subject><subject>PE61101E</subject><subject>QUERY ACCURACY</subject><subject>RECOGNITION</subject><subject>SCENE MODELING</subject><subject>SENSING GEOMETRY</subject><subject>THREE DIMENSIONAL</subject><subject>TRACKING</subject><subject>VIDEO BASED OBJECT TRACKING</subject><subject>VIDEO IMAGES</subject><subject>WUAFRL100011Y015</subject><fulltext>true</fulltext><rsrctype>report</rsrctype><creationdate>2015</creationdate><recordtype>report</recordtype><sourceid>1RU</sourceid><recordid>eNqFyTEKwkAQRuFtLES9gcUcIDYRDJYhKgpiofZh2P03LCSzYXbujxb2Vu-Dt3T3R5aZlSeYJk9PzIoCMbaUpVDMSjcxDMqG8GWEQjwq6rKY5rEilkAvSEkyrN0i8liw-XXltpfzu7vugiXfF0sC69tTe6ibujnu_-wPorIzCg</recordid><startdate>20151001</startdate><enddate>20151001</enddate><creator>Fisher, John</creator><creator>Darrell, Trevor</creator><creator>Galup, Luis</creator><creator>How, Jon</creator><creator>Krause, Andreas</creator><creator>Soatto, Stefano</creator><scope>1RU</scope><scope>BHM</scope></search><sort><creationdate>20151001</creationdate><title>Nonparametric Representations for Integrated Inference, Control, and Sensing</title><author>Fisher, John ; Darrell, Trevor ; Galup, Luis ; How, Jon ; Krause, Andreas ; Soatto, Stefano</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-dtic_stinet_ADA6272793</frbrgroupid><rsrctype>reports</rsrctype><prefilter>reports</prefilter><language>eng</language><creationdate>2015</creationdate><topic>COMPUTER LOGIC</topic><topic>Cybernetics</topic><topic>DATA FUSION</topic><topic>DISTRIBUTED LEARNING</topic><topic>INFERENCE</topic><topic>INFORMATION THEORY</topic><topic>MSEE PROGRAM</topic><topic>Operations Research</topic><topic>PE61101E</topic><topic>QUERY ACCURACY</topic><topic>RECOGNITION</topic><topic>SCENE MODELING</topic><topic>SENSING GEOMETRY</topic><topic>THREE DIMENSIONAL</topic><topic>TRACKING</topic><topic>VIDEO BASED OBJECT TRACKING</topic><topic>VIDEO IMAGES</topic><topic>WUAFRL100011Y015</topic><toplevel>online_resources</toplevel><creatorcontrib>Fisher, John</creatorcontrib><creatorcontrib>Darrell, Trevor</creatorcontrib><creatorcontrib>Galup, Luis</creatorcontrib><creatorcontrib>How, Jon</creatorcontrib><creatorcontrib>Krause, Andreas</creatorcontrib><creatorcontrib>Soatto, Stefano</creatorcontrib><creatorcontrib>MASSACHUSETTS INST OF TECH CAMBRIDGE COMPUTER SCIENCE AND ARTIFICIAL INTELLIGENCE LAB</creatorcontrib><collection>DTIC Technical Reports</collection><collection>DTIC STINET</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Fisher, John</au><au>Darrell, Trevor</au><au>Galup, Luis</au><au>How, Jon</au><au>Krause, Andreas</au><au>Soatto, Stefano</au><aucorp>MASSACHUSETTS INST OF TECH CAMBRIDGE COMPUTER SCIENCE AND ARTIFICIAL INTELLIGENCE LAB</aucorp><format>book</format><genre>unknown</genre><ristype>RPRT</ristype><btitle>Nonparametric Representations for Integrated Inference, Control, and Sensing</btitle><date>2015-10-01</date><risdate>2015</risdate><abstract>The objective of this research program was to develop mathematical foundations of information gathering through an integrated theory of sensing, inference, and control. The goal of the team was to develop a new framework for autonomous operations that will extend the state of the art in distributed learning and modeling from data, and tightly integrate these models into new decentralized cooperative planning algorithms. The main output of this effort will be a fundamental theory to integrate decentralized information driven planning methods for heterogenous teams with nonparametric Bayesian models of uncertainty. The feasibility and aspects of the value of the theory were demonstrated via integrated software and hardware experiments.
Prepared in cooperation with University of California - Los Angeles (UCLA), ETH Zurich, BAE Systems, and International Computer Science Institute (ICSI). Sponsored in part by DARPA.</abstract><oa>free_for_read</oa></addata></record> |
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source | DTIC Technical Reports |
subjects | COMPUTER LOGIC Cybernetics DATA FUSION DISTRIBUTED LEARNING INFERENCE INFORMATION THEORY MSEE PROGRAM Operations Research PE61101E QUERY ACCURACY RECOGNITION SCENE MODELING SENSING GEOMETRY THREE DIMENSIONAL TRACKING VIDEO BASED OBJECT TRACKING VIDEO IMAGES WUAFRL100011Y015 |
title | Nonparametric Representations for Integrated Inference, Control, and Sensing |
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