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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Fisher, John, Darrell, Trevor, Galup, Luis, How, Jon, Krause, Andreas, Soatto, Stefano
Format: Report
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
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.
format Report
fullrecord <record><control><sourceid>dtic_1RU</sourceid><recordid>TN_cdi_dtic_stinet_ADA627279</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>ADA627279</sourcerecordid><originalsourceid>FETCH-dtic_stinet_ADA6272793</originalsourceid><addsrcrecordid>eNqFyTEKwkAQRuFtLES9gcUcIDYRDJYhKgpiofZh2P03LCSzYXbujxb2Vu-Dt3T3R5aZlSeYJk9PzIoCMbaUpVDMSjcxDMqG8GWEQjwq6rKY5rEilkAvSEkyrN0i8liw-XXltpfzu7vugiXfF0sC69tTe6ibujnu_-wPorIzCg</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>report</recordtype></control><display><type>report</type><title>Nonparametric Representations for Integrated Inference, Control, and Sensing</title><source>DTIC Technical Reports</source><creator>Fisher, John ; Darrell, Trevor ; Galup, Luis ; How, Jon ; Krause, Andreas ; Soatto, Stefano</creator><creatorcontrib>Fisher, John ; Darrell, Trevor ; Galup, Luis ; How, Jon ; Krause, Andreas ; Soatto, Stefano ; MASSACHUSETTS INST OF TECH CAMBRIDGE COMPUTER SCIENCE AND ARTIFICIAL INTELLIGENCE LAB</creatorcontrib><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><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>
fulltext fulltext_linktorsrc
identifier
ispartof
issn
language eng
recordid cdi_dtic_stinet_ADA627279
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
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-08T14%3A54%3A09IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-dtic_1RU&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=unknown&rft.btitle=Nonparametric%20Representations%20for%20Integrated%20Inference,%20Control,%20and%20Sensing&rft.au=Fisher,%20John&rft.aucorp=MASSACHUSETTS%20INST%20OF%20TECH%20CAMBRIDGE%20COMPUTER%20SCIENCE%20AND%20ARTIFICIAL%20INTELLIGENCE%20LAB&rft.date=2015-10-01&rft_id=info:doi/&rft_dat=%3Cdtic_1RU%3EADA627279%3C/dtic_1RU%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true