Skip to main content
Springer Nature Link
Log in

Sparse Adaptive Channel Estimation Based on Multi-kernel Correntropy

  • Conference paper
  • First Online:
Intelligent Robotics and Applications (ICIRA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14273))

Included in the following conference series:

  • 1010 Accesses

Abstract

The communication channel estimation between unmanned systems has always been a concern of researchers, especially the channel estimation of broadband wireless communication and underwater acoustic communication. Due to its sparsity, more and more researchers use adaptive filtering algorithms combined with sparse constraints for sparse channel estimation. In this paper, a sparse adaptive filtering algorithm based on correntropy induced metric (CIM) penalty is proposed, and the maximum multi-kernel correntropy criterion (MMKCC) is used to replace the minimum mean square error criterion and the maximum correntropy criteria for robust channel estimation. Specifically, the MMKCC is used to suppress complex impulse noise, and the CIM is used to effectively utilize channel sparsity. The effectiveness of the proposed method is confirmed by computer simulation.

This study was founded by the National Natural Science Foundation of China with Grant 51975107, 62076096, and Sichuan Science and Technology Major Project No. 2022ZDZX0039, No.2019ZDZX0020, and Sichuan Science and Technology Program No. 2022YFG0343, No. 23ZDYF0738.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Cai, Y., Guo, H., Zhou, K., Xu, L.: Unmanned aerial vehicle cluster operations under the background of intelligentization. In: 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM), pp. 525–529. IEEE (2021)

    Google Scholar 

  2. Chen, Y.L., Ma, X.W., Bai, G.Q., Sha, Y., Liu, J.: Multi-autonomous underwater vehicle formation control and cluster search using a fusion control strategy at complex underwater environment. Ocean Eng. 216, 108048 (2020)

    Article  MATH  Google Scholar 

  3. Beena, A., Pillai, S.S., Vijayakumar, N.: An improved adaptive sparse channel estimation method for next generation wireless broadband. In: 2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), pp. 1–5. IEEE (2018)

    Google Scholar 

  4. Şenol, H.: Joint channel estimation and symbol detection for OFDM systems in rapidly time-varying sparse multipath channels. Wireless Pers. Commun. 82, 1161–1178 (2015)

    Article  MATH  Google Scholar 

  5. Matz, G., Hlawatsch, F.: Time-varying communication channels: fundamentals, recent developments, and open problems. In: 2006 14th European Signal Processing Conference, pp. 1–5. IEEE (2006)

    Google Scholar 

  6. Chen, Y., Gu, Y., Hero, A.O.: Sparse LMS for system identification. In: 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3125–3128. IEEE (2009)

    Google Scholar 

  7. Hong, X., Gao, J., Chen, S.: Zero-attracting recursive least squares algorithms. IEEE Trans. Veh. Technol. 66(1), 213–221 (2016)

    MATH  Google Scholar 

  8. Chen, B., Liu, X., Zhao, H., Principe, J.C.: Maximum correntropy Kalman filter. Automatica 76, 70–77 (2017). https://doi.org/10.1016/j.automatica.2016.10.004

    Article  MathSciNet  MATH  Google Scholar 

  9. Seth, S., Principe, J.C.: Compressed signal reconstruction using the correntropy induced metric. In: 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3845–3848 (2008). https://doi.org/10.1109/ICASSP.2008.4518492

  10. Ma, W., Qu, H., Gui, G., Xu, L., Zhao, J., Chen, B.: Maximum correntropy criterion based sparse adaptive filtering algorithms for robust channel estimation under non-gaussian environments. J. Franklin Inst. 352(7), 2708–2727 (2015)

    Article  MATH  Google Scholar 

  11. Lu, M., Xing, L., Zheng, N., Chen, B.: Robust sparse channel estimation based on maximum mixture correntropy criterion. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1–6. IEEE (2020)

    Google Scholar 

  12. Zhang, X., Li, K., Wu, Z., Fu, Y., Zhao, H., Chen, B.: Convex regularized recursive maximum correntropy algorithm. Signal Process. 129, 12–16 (2016)

    Article  MATH  Google Scholar 

  13. Chen, B., Xie, Y., Wang, X., Yuan, Z., Ren, P., Qin, J.: Multikernel correntropy for robust learning. IEEE Trans. Cybern. 52(12), 13500–13511 (2022). https://doi.org/10.1109/TCYB.2021.3110732

    Article  MATH  Google Scholar 

  14. Weng, B., Barner, K.E.: Nonlinear system identification in impulsive environments. IEEE Trans. Signal Process. 53(7), 2588–2594 (2005)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Electronic Science and Technology of China, Chengdu, 611731, China

    Kun Zhang, Gang Wang, Mingzhu Wei, Chen Xu & Bei Peng

Authors
  1. Kun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mingzhu Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chen Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bei Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bei Peng .

Editor information

Editors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Huayong Yang

  2. Harbin Institute of Technology, Shenzhen, China

    Honghai Liu

  3. Zhejiang University, Hangzhou, China

    Jun Zou

  4. Huazhong University of Science and Technology, Wuhan, China

    Zhouping Yin

  5. Shenyang Institute of Automation, Shenyang, Liaoning, China

    Lianqing Liu

  6. Zhejiang University, Hangzhou, China

    Geng Yang

  7. Zhejiang University, Hangzhou, China

    Xiaoping Ouyang

  8. Harbin Institute of Technology, Shenzhen, China

    Zhiyong Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, K., Wang, G., Wei, M., Xu, C., Peng, B. (2023). Sparse Adaptive Channel Estimation Based on Multi-kernel Correntropy. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14273. Springer, Singapore. https://doi.org/10.1007/978-981-99-6498-7_11

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-6498-7_11

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-6497-0

  • Online ISBN: 978-981-99-6498-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics