Wireless Indoor Location Estimation Based on Neural Network RSS Signature Recognition (LENSR)

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Location Based Services (LBS), context aware applications, and people and object tracking depend on the ability to locate mobile devices, also known as localization, in the wireless landscape. Localization enables a diverse set of applications that include, but are not limited to, vehicle guidance in an industrial environment, security monitoring, self-guided tours, personalized communications services, resource tracking, mobile commerce services, guiding emergency workers during fire emergencies, habitat monitoring, environmental surveillance, and receiving alerts. This paper presents a new neural network approach (LENSR) based on a competitive topological Counter Propagation Network (CPN) with k-nearest neighborhood vector mapping, for indoor location estimation ... continued below

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Derr, Kurt & Manic, Milos June 1, 2008.

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Location Based Services (LBS), context aware applications, and people and object tracking depend on the ability to locate mobile devices, also known as localization, in the wireless landscape. Localization enables a diverse set of applications that include, but are not limited to, vehicle guidance in an industrial environment, security monitoring, self-guided tours, personalized communications services, resource tracking, mobile commerce services, guiding emergency workers during fire emergencies, habitat monitoring, environmental surveillance, and receiving alerts. This paper presents a new neural network approach (LENSR) based on a competitive topological Counter Propagation Network (CPN) with k-nearest neighborhood vector mapping, for indoor location estimation based on received signal strength. The advantage of this approach is both speed and accuracy. The tested accuracy of the algorithm was 90.6% within 1 meter and 96.4% within 1.5 meters. Several approaches for location estimation using WLAN technology were reviewed for comparison of results.

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  • 3rd IEEE Conference on Industrial Electronics and Applications,Singapore,06/03/2008,06/05/2008

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  • Report No.: INL/CON-07-13642
  • Grant Number: DE-AC07-99ID-13727
  • Office of Scientific & Technical Information Report Number: 935448
  • Archival Resource Key: ark:/67531/metadc894963

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  • June 1, 2008

Added to The UNT Digital Library

  • Sept. 27, 2016, 1:39 a.m.

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  • Nov. 7, 2017, 6:13 p.m.

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Derr, Kurt & Manic, Milos. Wireless Indoor Location Estimation Based on Neural Network RSS Signature Recognition (LENSR), article, June 1, 2008; [Idaho Falls, Idaho]. (digital.library.unt.edu/ark:/67531/metadc894963/: accessed April 21, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.