Probabilistic Framework for Sensor Management

A probabilistic sensor management framework is introduced, which maximizes the utility of sensor systems with many different sensing modalities by dynamically configuring the sensor system in the most beneficial way. For this purpose, techniques from stochastic control and Bayesian estimation are co...

Full description

Saved in:
Bibliographic Details
Main Author: Huber, Marco
Format: Online
Language:English
Published: KIT Scientific Publishing 2021
Subjects:
Online Access:34253
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1869524493800570880
author Huber, Marco
author_browse Huber, Marco
author_facet Huber, Marco
author_sort Huber, Marco
collection Directory of Open Access Books
description A probabilistic sensor management framework is introduced, which maximizes the utility of sensor systems with many different sensing modalities by dynamically configuring the sensor system in the most beneficial way. For this purpose, techniques from stochastic control and Bayesian estimation are combined such that long-term effects of possible sensor configurations and stochastic uncertainties resulting from noisy measurements can be incorporated into the sensor management decisions.
format Online
id doab-20.500.12854ir-57004
institution Directory of Open Access Books
language eng
publishDate 2021
publishDateRange 2021
publishDateSort 2021
publisher KIT Scientific Publishing
publisherStr KIT Scientific Publishing
record_format ojs
spelling doab-20.500.12854ir-570042023-12-20T18:40:48Z Probabilistic Framework for Sensor Management Huber, Marco QA75.5-76.95 Bayesian estimation decision theory sensor management information theory Gaussian mixtures bic Book Industry Communication::U Computing & information technology::UY Computer science A probabilistic sensor management framework is introduced, which maximizes the utility of sensor systems with many different sensing modalities by dynamically configuring the sensor system in the most beneficial way. For this purpose, techniques from stochastic control and Bayesian estimation are combined such that long-term effects of possible sensor configurations and stochastic uncertainties resulting from noisy measurements can be incorporated into the sensor management decisions. 2021-02-11T23:54:57Z 2021-02-11T23:54:57Z 2019-07-30 19:59:17 2009 book 34253 18673813 9783866444058 https://directory.doabooks.org/handle/20.500.12854/57004 eng Karlsruhe Series on Intelligent Sensor-Actuator-Systems / Universität Karlsruhe, Intelligent Sensor-Actuator-Systems Laboratory image/jpeg Attribution-NonCommercial-NoDerivatives 4.0 International https://www.ksp.kit.edu/9783866444058 KIT Scientific Publishing 10.5445/KSP/1000012224 10.5445/KSP/1000012224 68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2 9783866444058 VI, 159 p. open access
spellingShingle QA75.5-76.95
Bayesian estimation
decision theory
sensor management
information theory
Gaussian mixtures
bic Book Industry Communication::U Computing & information technology::UY Computer science
Huber, Marco
Probabilistic Framework for Sensor Management
title Probabilistic Framework for Sensor Management
title_full Probabilistic Framework for Sensor Management
title_fullStr Probabilistic Framework for Sensor Management
title_full_unstemmed Probabilistic Framework for Sensor Management
title_short Probabilistic Framework for Sensor Management
title_sort probabilistic framework for sensor management
topic QA75.5-76.95
Bayesian estimation
decision theory
sensor management
information theory
Gaussian mixtures
bic Book Industry Communication::U Computing & information technology::UY Computer science
topic_facet QA75.5-76.95
Bayesian estimation
decision theory
sensor management
information theory
Gaussian mixtures
bic Book Industry Communication::U Computing & information technology::UY Computer science
url 34253
work_keys_str_mv AT hubermarco probabilisticframeworkforsensormanagement