Wireless Personal Communications

, Volume 109, Issue 3, pp 1581–1604 | Cite as

Energy-Based Timing Estimation and Artificial Neural Network Based Ranging Error Mitigation in mm-Wave Ranging Systems Using Statistics Fingerprint Analysis

  • Xiaolin LiangEmail author
  • Thomas Aaron Gulliver


Non-line-of-sight (NLOS) and dense multipath problems are the major challenges for the millimeter wave (mm-wave) indoor ranging systems. To acquire time of arrival (TOA) estimate accurately in such a poor environment, an improved statistics fingerprint analysis algorithm for energy-based timing estimation and artificial neural network (ANN) based ranging error mitigation is presented in this paper. The developed algorithm can obtain TOA estimate accurately by measuring the kurtosis, skewness, standard deviation, minimum slope, and gradient of the received mm-wave pulses. ANN is employed to mitigate the ranging error based on the obtained nonlinear regression between the thresholds and the analyzed characteristics of mm-wave pulses. The presented numerical simulation results indicate the proposed algorithm can achieve significant performance improvements in both line of sight and NLOS channels of the IEEE 802.15.3c standard, as compared to conventional algorithms.


Non-line-of-sight (NLOS) Millimeter wave (mm-wave) Time of arrival (TOA) Statistics fingerprint analysis (SFA) Artificial neural network (ANN) 



The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the article.


This research was funded by the National Defense Innovation Special Zone Project (***005-01), National Key Research and Development Program (2018YFF0109302 and 2018YFF0109702), China Electronics Technology Group Corporation Innovation Fund (KJ1701008) and Science and Technology on Electronic Test & Measurement Laboratory (614200103010117 and 614200105010217).

Compliance with Ethical Standards

Conflict of interest

The authors declare no conflict of interest.


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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Science and Technology on Electronic Test and Measurement LaboratoryThe 41st Research Institute of CETCQingdaoChina
  2. 2.Department of Electrical Computer EngineeringUniversity of VictoriaVictoriaCanada

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