Skip to main content

Advertisement

SpringerLink
Log in

An Improved Statistics Fingerprint Analysis Method for Time Delay Estimation in Multipath NLOS Environment

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

This paper focuses on time of arrival (TOA) measurement for wireless personal area network (WPAN) under the circumstances that dense multipath and non-line of sight (NLOS), which exploits a non-coherent receiver for millimeter wave (mm-Wave) pulses. For the localization enhancement, an improved statistics fingerprint analysis (SFA) method is presented for energy-assisted TOA estimate and the ranging error mitigation using artificial neural network (ANN) by fourth order cumulants (FOCs) technique. The corresponding mean absolute error (MAE) is utilized as a measurement to access the performance of the developed method. Simulation results indicate SFA can outperform TOA estimation scheme significantly proposed in WPAN previously, show that SFA can effectively enhance the accuracy of TOA estimates evidently. The advantages of SFA such as low complexity implementation, fast convergence, high accuracy, etc. make it an attractive positioning method in WPAN compared with a coherent receiver.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Chu, Y., He, L., & Yao, F. (2016). An improved localization algorithm of taylor series expansion search target. Optik, 127(19), 8070–8075. https://doi.org/10.1016/j.ijleo.2016.05.084

    Article  Google Scholar 

  2. Li, S., Liu, K., & Xiao, L. (2014). Fleet algorithm for X-ray pulsar profile construction and TOA solution based on compressed sensing. Optik, 125(7), 1875–1879. https://doi.org/10.1016/j.ijleo.2013.10.032

    Article  Google Scholar 

  3. Kang, Z., He, H., Liu, J., Xin, M., & Gui, M. (2018). Adaptive pulsar time delay estimation using wavelet-based RLS. Optik, 171, 266–276. https://doi.org/10.1016/j.ijleo.2018.05.118

    Article  Google Scholar 

  4. Miorandi, D., Sicari, S., Pellegrini, F. D., & Chlamtac, I. (2012). Internet of Things: Vision, applications and research challenges. Ad Hoc Networks, 10(7), 1497–1516. https://doi.org/10.1016/j.adhoc.2012.02.016

    Article  Google Scholar 

  5. Sun, W. F., Wang, Z., Ma, H. Y., Zhao, L., & LI, Q.R. (2012). Key technologies and typical military application of IoT. Internet things technol. https://doi.org/10.3969/j.issn.1002-137X.2010.06.001

    Article  Google Scholar 

  6. Chatzigiannakis, I., Drude, J.P., Hasemann, H., & Kröller, A. (2014). Developing smart homes using the Internet of Things: How to demonstrate your system, International Conference on Distributed, Ambient, and Pervasive Interactions, Cham, Switzerland: Springer, 2014: 415-426.

  7. Uckelmann, D., Harrison, M., & Michahelles, F. (2011). Architecting the Internet of Things. Springer.

    Book  Google Scholar 

  8. Swan, M. (2012). Sensor mania! The Internet of Things, wearable computing, objective metrics, and the quantified self 2.0, Journal of Sensor and Actuator Networks. 1(3), 217–253. https://doi.org/10.3390/jsan1030217

  9. Hazra, R., & Tyagi, A. (2014). A survey on various coherent and non-coherent IR-UWB receivers. Wireless Personal Communications., 79(3), 2339–2369. https://doi.org/10.1007/s11277-014-1988-4

    Article  Google Scholar 

  10. Zhang, H., Cui, X., & Gulliver, T. A. (2011). Threshold selection for ultra-wideband TOA estimation based on skewness analysis. Springer-Verlag Lecture Notes in Computer Science., 6905, 503–513. https://doi.org/10.1007/978-3-642-23641-9_40

    Article  Google Scholar 

  11. Guvenc, I., & Sahinoglu, Z. (2005). Threshold selection for UWB TOA estimation based on kurtosis analysis. IEEE Communications Letters., 9(12), 1025–1027. https://doi.org/10.1109/LCOMM.2005.1576576

    Article  Google Scholar 

  12. Zhang, H., Cui, X., & Gulliver, T. A. (2012). Remotely-sensed TOA interpretation of synthetic UWB based on neural networks. EURASIP Journal on Advances in Signal Processing., 185(1), 13. https://doi.org/10.1186/1687-6180-2012-185

    Article  Google Scholar 

  13. Feng, W., Zhi, T., & Sadler, B. (2012). Weighted energy detection for noncoherent ultra-wideband receiver design. IEEE Transaction on Wireless Communications. 10 (2), 710–720. https://doi.org/10.1109/TWC.2010.120310.101390

  14. Mendel, J. (1991). Tutorial on higher-order statistics (spectra) in signal processing and system theory: Theoretical results and some applications. Proceedings of the IEEE., 79(3), 278–305. https://doi.org/10.1109/5.75086

    Article  Google Scholar 

  15. Ding, Y., Wang, Y., & Zhou, D. (2017). Mortality prediction for ICU patients combining just-in-time learning and extreme learning machine. Neurocomputing, 281, 12–19. https://doi.org/10.1016/j.neucom.2017.10.044

    Article  Google Scholar 

  16. Hou, C., Nie, F., Zhang, C., Yi, D., & Wu, Y. (2014). Multiple rank multi-linear SVM for matrix data classification. Pattern Recognition., 47(1), 454–469. https://doi.org/10.1016/j.patcog.2013.07.002

    Article  MATH  Google Scholar 

  17. Wang, Y., Dong, Z., Li, Z., & Ding, S. X. (2017). Unbiased minimum variance fault and state estimation for linear discrete time-varying two-dimensional systems. IEEE Transactions on Automatic Control, 62(10), 5463–5469. https://doi.org/10.1109/TAC.2017.2697210

    Article  MathSciNet  MATH  Google Scholar 

  18. Liang, X., Zhang, H., & Gulliver, T. A. (2016). Energy detector based time of arrival estimation using a neural network with millimeter wave signals. KSII Transactions on Internet and Information Systems, 10(7), 3050–3065. https://doi.org/10.3837/tiis.2016.07.010

    Article  Google Scholar 

  19. Zafar, A., & Hong, K. (2017). Detection and classification of three-class initial dips from prefrontal cortex. Biomedical Optics Express., 8(1), 367–383. https://doi.org/10.1364/BOE.8.000367

    Article  Google Scholar 

  20. Liang, X., Zhang, H., Lu, T., & Gulliver, T. A. (2017). Energy detector based TOA estimation for MMW systems using machine learning. Telecommunication Systems., 64(2), 417–427. https://doi.org/10.1007/s11235-016-0182-2

    Article  Google Scholar 

  21. Liang, X., Zhang, H., Gulliver, T. A., Fang, G., & Ye, S. (2017). An improved algorithm for through-wall target detection using ultra-wideband impulse radar. IEEE Access., 7(5), 22101–22118. https://doi.org/10.1109/ACCESS.2017.2761771

    Article  Google Scholar 

  22. Oh, D., Kim, S., Yoon, Y. H., Chong, J. W., & Daegun, O. (2013). Two dimensional ESPRIT-like shift-invariant ToA estimation algorithm using multi-band chirp signals robust to carrier frequency offset. IEEE Transactions on Wireless Communications., 2(7), 3130–3139. https://doi.org/10.1109/TWC.2013.060413.112013

    Article  Google Scholar 

  23. Stoica, P., Hao, H., & Jian, L. (2009). New algorithms for designing unimodular sequences with good correlation properties. IEEE Transactions on Signal Processing., 57(4), 1415–1425. https://doi.org/10.1109/TSP.2009.2012562

    Article  MathSciNet  MATH  Google Scholar 

  24. Hao, H., Stoica, P., & Jian, J. (2009). Designing unimodular sequence sets with good correlations, including an application to MIMO radar. IEEE Transactions on Signal Processing., 57(11), 4391–4405. https://doi.org/10.1109/TSP.2009.2025108

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

The authors declare that there is no conflict of interest regarding the publication of this manuscript. This work was supported by the Nature Science Foundation of China under Grant No. 41527901.

Author information

Authors and Affiliations

  1. Science and Technology on Electronic Test and Measurement Laboratory, The 41st Research Institute of CETC, Xiang Jiang Road 98th, Huangdao District, Qingdao, 266555, China

    Xiaolin Liang, Weifeng Zhu & Jianqin Deng

Authors
  1. Xiaolin Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weifeng Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jianqin Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jianqin Deng.

Ethics declarations

Conflict of interest

The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liang, X., Zhu, W. & Deng, J. An Improved Statistics Fingerprint Analysis Method for Time Delay Estimation in Multipath NLOS Environment. Wireless Pers Commun 123, 1855–1869 (2022). https://doi.org/10.1007/s11277-021-09217-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-09217-1

Keywords

Search

Navigation