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Remotely-sensed TOA interpretation of synthetic UWB based on neural networks

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dc.contributor.author Zhang, Hao
dc.contributor.author Cui, Xue-rong
dc.contributor.author Gulliver, T Aaron
dc.date.accessioned 2014-06-04T21:57:14Z
dc.date.available 2014-06-04T21:57:14Z
dc.date.copyright 2012 en_US
dc.date.issued 2012-08-25
dc.identifier.citation Zhang et al.: Remotely-sensed TOA interpretation of synthetic UWB based on neural networks. EURASIP Journal on Advances in Signal Processing 2012 2012:185. en_US
dc.identifier.uri http://asp.eurasipjournals.com/content/2012/1/185
dc.identifier.uri http://dx.doi.org/10.1186/1687-6180-2012-185
dc.identifier.uri http://hdl.handle.net/1828/5426
dc.description SpringerOpen en_US
dc.description.abstract Because of the good penetration into many common materials and inherent fine resolution, Ultra-Wideband (UWB) signals are widely used in remote sensing applications. Typically, accurate Time of Arrival (TOA) estimation of the UWB signals is very important. In order to improve the precision of the TOA estimation, a new threshold selection algorithm using Artificial Neural Networks (ANN) is proposed which is based on a joint metric of the skewness and maximum slope after Energy Detection (ED). The best threshold based on the signal-to-noise ratio (SNR) is investigated and the effects of the integration period and channel model are examined. Simulation results are presented which show that for the IEEE802.15.4a channel models CM1 and CM2, the proposed ANN algorithm provides better precision and robustness in both high and low SNR environments than other ED-based algorithms. Keywords: Artificial Neural Network (ANN), Remote sensing, Ultra-Wideband (UWB), TOA estimation, Ranging, Skewness en_US
dc.language.iso en en_US
dc.publisher Springer Open en_US
dc.title Remotely-sensed TOA interpretation of synthetic UWB based on neural networks en_US
dc.type Article en_US
dc.description.scholarlevel Faculty en_US
dc.description.reviewstatus Reviewed en_US


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