Threshold Selection for Ultra-Wideband TOA Estimation Based on Skewness Analysis

Zhang, Hao; Cui, Xuerong; Gulliver, T. Aaron

doi:10.1007/978-3-642-23641-9_40

Cited by 10 publications

(18 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…is the number of samples. The integrator output is then given by  when the received signal is only noise, and (9) It is difficult to determine the threshold directly from the received energy block values, so a normalized threshold is employed which is given by…”

Section: System Modelmentioning

confidence: 99%

“…Many TOA estimation algorithms have been developed [9][10] [11][12] [13]. The majority of these algorithms employ either a coherent receiver such as a matched filter (MF), or a non-coherent receiver such as an energy detector (ED).…”

Section: Introductionmentioning

confidence: 99%

“…Many ED based threshold selection algorithms have been developed. A threshold selection algorithm for TOA estimation based on the skewness of the energy block values was proposed in [9]. In [10], a normalized threshold based on the kurtosis of these values was introduced.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Energy Detector based Time of Arrival Estimation using a Neural Network with Millimeter Wave Signals

2016

KSII TIIS

View full text Add to dashboard Cite

Neural networks (NNs) are extensively used in applications requiring signal classification and regression analysis. In this paper, a NN based threshold selection algorithm for 60 GHz millimeter wave (MMW) time of arrival (TOA) estimation using an energy detector (ED) is proposed which is based on the skewness, kurtosis, and curl of the received energy block values. The best normalized threshold for a given signal-to-noise ratio (SNR) is determined, and the influence of the integration period and channel on the performance is investigated. Results are presented which show that the proposed NN based algorithm provides superior precision and better robustness than other ED based algorithms over a wide range of SNR values. Further, it is independent of the integration period and channel model.

show abstract

Section: System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Energy Detector based Time of Arrival Estimation using a Neural Network with Millimeter Wave Signals

2016

KSII TIIS

View full text Add to dashboard Cite

show abstract

“…As the SNR increases, S tends to increase. In [6], exponential functions were fit to the skewness results for T b = 1 ns and T b = 4 ns, with S as the x- …”

Section: Skewnessmentioning

confidence: 99%

“…However, this is a very challenging problem due to the severe environments encountered, e.g., thermal noise, multi-path fading, reflection interference, and inter-symbol interference. The TOA estimation problem has extensively been studied [3][4][5][6]. There are two approaches applicable to UWB technology, a Matched Filter (MF) [3] (such as a Rake or correlation receiver) with a high sampling rate and high-precision correlation, or an Energy Detector (ED) [4][5][6] with a lower sampling rate and low complexity.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Cui

Gulliver

2012

EURASIP J. Adv. Signal Process.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Energy detector based TOA estimation for MMW systems using machine learning

Liang

Zhang

et al. 2016

Telecommun Syst

View full text Add to dashboard Cite

Threshold Selection for Ultra-Wideband TOA Estimation Based on Skewness Analysis

Cited by 10 publications

References 7 publications

Energy Detector based Time of Arrival Estimation using a Neural Network with Millimeter Wave Signals

Energy Detector based Time of Arrival Estimation using a Neural Network with Millimeter Wave Signals

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

Energy detector based TOA estimation for MMW systems using machine learning

Contact Info

Product

Resources

About