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

Hao Zhang13*, Xue-rong Cui12 and T Aaron Gulliver3

Author Affiliations

1 Department of Information Science and Engineering, Ocean University of China, Qing Dao, China

2 Department of Computer and Communication Engineering, China University of Petroleum (East Chinxa), Qing Dao, China

3 Department of Electrical Computer Engineering, University of Victoria, Victoria, Canada

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EURASIP Journal on Advances in Signal Processing 2012, 2012:185  doi:10.1186/1687-6180-2012-185

Published: 25 August 2012


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.

Artificial Neural Network (ANN); Remote sensing; Ultra-Wideband (UWB); TOA estimation; Ranging; Skewness