LEADER 01690nam  2200373z- 450 
001 9910346922203321
005 20210211
010   $a1000012224
035   $a(CKB)4920000000101300
035   $a(oapen)https://directory.doabooks.org/handle/20.500.12854/57004
035   $a(oapen)doab57004
035   $a(EXLCZ)994920000000101300
100   $a20202102d2009      |y         0
101 0 $aeng
135   $aurmn|---annan
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 00$aProbabilistic Framework for Sensor Management
210   $cKIT Scientific Publishing$d2009
215   $a1 online resource (VI, 159 p. p.)
225 1 $aKarlsruhe Series on Intelligent Sensor-Actuator-Systems / Universität Karlsruhe, Intelligent Sensor-Actuator-Systems Laboratory
311 08$a3-86644-405-2 
330   $aA 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.
610   $aBayesian estimation
610   $adecision theory
610   $aGaussian mixtures
610   $ainformation theory
610   $asensor management
700   $aHuber$b Marco$4auth$01302254
906   $aBOOK
912   $a9910346922203321
996   $aProbabilistic Framework for Sensor Management$93026305
997   $aUNINA