FACILITY RFID LOCALIZATION SYSTEM BASED ON ARTIFICIAL NEURAL NETWORKS - Holland, William S.; Young II, William A.; Weckman, Gary R. (International Journal of Industrial Engineering: Theory, Applications and Practice, 2011)
<!–[if gte mso 9]> <![endif]–><!–[if gte mso 9]> Normal 0 false false false EN-US X-NONE X-NONE <![endif]–><!–[if gte mso 9]> <![endif]–> <!– /* Font Definitions */ @font-face font-family:"Cambria Math"; panose-1:2 4 5 3 5 4 6 3 2 4; mso-font-charset:0; mso-generic-font-family:roman; mso-font-pitch:variable; mso-font-signature:-1610611985 1107304683 0 0 415 0; /* Style Definitions */ p.MsoNormal, li.MsoNormal, div.MsoNormal mso-style-unhide:no; mso-style-qformat:yes; mso-style-parent:""; margin:0in; margin-bottom:.0001pt; text-align:justify; text-indent:.3in; mso-pagination:widow-orphan; tab-stops:.3in; font-size:10.0pt; mso-bidi-font-size:12.0pt; font-family:"Times New Roman","serif"; mso-fareast-font-family:"Times New Roman"; @page Section1 size:8.5in 11.0in; margin:1.0in 1.0in 1.0in 1.0in; mso-header-margin:.5in; mso-footer-margin:.5in; mso-paper-source:0; div.Section1 page:Section1; –> <!–[if gte mso 10]> <mce:style><! /* Style Definitions */ table.MsoNormalTable mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi; –> <!–[endif]–> Radio frequency identification (RFID) technology is used for asset tracking due to its accuracy and speed. RFID tracking systems are being used to locate tagged objects in indoor environments, however; reliability is low due to interferences. To overcome this limitation, artificial neural networks (ANNs) can be used to determining a device’s location in the proximity of interference. This research presents a proof of concept to an industrial application of using ANNs as an RFID localization algorithm when objects are subjected to metallic and human interference. To prove this concept, random samples are collected using the received signal strength indication (RSSI) values from passive RFID readers and antennas. The test results show that ANNs can determine the location of a passive RFID tag accurately in the presence of noise and shows that data preprocessing techniques can improve the predictive capabilities of the ANN-RFID localization algorithm.