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.
Highlights
As a new wireless communications technology, UltraWideband (UWB) has generated considerable research interest due to the many potential applications
Section “Statistical characteristics of the signal energy” considers the statistical characteristics of the energy values, and a joint metric based on skewness and maximum slope is proposed
These results show that the relationship between the two parameters is not affected significantly by the channel model, but is more dependent on the integration period, so the values for channels CM1 and CM2 can be combined
Summary
As a new wireless communications technology, UltraWideband (UWB) has generated considerable research interest due to the many potential applications. The TOA estimate is obtained using Equation (11) The problem in this case becomes one of how to set the threshold, i.e., how to establish the relationship between the received energy values and ξnorm. In [4], a normalized threshold selection technique for TOA estimation of UWB signals was proposed which uses exponential and linear curve fitting of the kurtosis of the received samples. An ANN algorithm is employed to obtain the normalized threshold based on the signal energy statistics.
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