Transcription of On Data Fusion for Wireless Localization
{{id}} {{{paragraph}}}
Example: air traffic controller
On Data Fusion for Wireless Localization
1On data Fusion for Wireless LocalizationRobin Wentao Ouyang, Albert Kai-Sun Wong, Mung ChiangAbstract This paper presents a data Fusion framework forwireless Localization via the weighted least square estimator(WLSE). Three types of Fusion schemes are presented: mea-surement Fusion , estimate Fusion and mixed Fusion . Theoreticalperformance comparison among these schemes in terms of theestimation error covariance matrix is conducted. We show that, ifthe raw measurement vectors are correlated, then measurementfusion achieves the best performance, followed by mixed fusionand estimate Fusion is the worst. If the raw measurement vectorsare uncorrelated, then they can achieve the same benefits that can be earned from data Fusion are alsoinvestigated and numerical case studies are presented to validateour theoretical Terms Wireless Localization , data Fusion , weighted leastsquare estimator (WLSE), Cramer-Rao lower bound (CRLB).
1 On Data Fusion for Wireless Localization Robin Wentao Ouyang, Albert Kai-Sun Wong, Mung Chiang Abstract—This paper presents a data fusion framework for wireless localization via the weighted least square estimator
Loading..
Tags:
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document:
Related search queries
Vehicle Localization in Urban Environments, Vehicle Localization in Urban Environments Using Probabilistic Maps, Localization, AWIPS II Localization, National Weather Service, Sensor Networks Chapter 9: Localization, Sensor Networks Chapter 9: Localization & positioning, OverFeat: Integrated Recognition, Localization and, Learning Deep Features for Discriminative Localization, Object detection and localization using local, WiFi Localization and Navigation for Autonomous