Skip to main content
SpringerLink
Log in

PSO-RBF Small Target Detection in Sea Clutter Background

  • Conference paper
  • First Online:
Communications, Signal Processing, and Systems (CSPS 2018)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 516))

  • 2190 Accesses

Abstract

Target detection in the background of sea clutter is an important part of sea surface radar signal processing. The traditional detection of weak targets in sea clutter is based on the statistical characteristics of sea clutter, which does not reflect the intrinsic dynamics of sea clutter. Therefore, the detection results are not ideal. Based on the chaotic characteristics of sea clutter, this dissertation reconstructs the space structure of the sea clutter and proposes an improved particle swarm optimization (PSO) algorithm based on adaptive time-varying weights and local search operators. This method was applied to the optimization learning of the parameters of the radial basis function (RBF) neural network kernel function. The method was validated by using McIX University in Canada to measure the sea clutter data with the target in the Dartmouth area using IPIX radar. The results showed that the PSO-RBF algorithm in the background of chaotic sea clutter has good predictability. Compared with the general radial basis neural network, the improved algorithm not only has fast convergence speed but also has high error accuracy.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Roy LP, Kumar RVR. Accurate K-distributed clutter model for scanning radar application. IET Radar Sonar Navigation. 2010;4(2):158–67.

    Article  Google Scholar 

  2. Hennsssey G, Leung H, Yip PC. Sea-clutter modeling using a radar-basis-function neural network. IEEE J Oceanic Eng. 2001;28(3):358–72.

    Article  Google Scholar 

  3. Leung H, Dubash N, Xie N. Detection of small objects in clutter using a GA-RBF neural network. IEEE Trans Aerosp Electron Syst. 2002;38(1):98–118.

    Article  Google Scholar 

  4. Hongyan JT. Weak signal detection based on dual constraint least squares support vector machine in Chaotic Sea clutter background. Acta Physica Sinica. 2010.

    Google Scholar 

  5. Si W, Tong N, Wang Q. Small target detection algorithm research in sea clutter background. Sig Process. 2014.

    Google Scholar 

  6. Wang F. Sea clutter fractal characteristic analysis, modeling and small target detection. Doctoral Dissertation. Harbin Engineering University; 2009.

    Google Scholar 

  7. Si W, Tong N, Wang Q. Small target detection algorithm in the sea clutter background. Sea clutter processing and target detection based on nonlinear analysis. Doctoral Dissertation. Dalian Maritime University; 2008.

    Google Scholar 

  8. Li D. Characteristic detection method for small and medium targets in sea clutter. Master Thesis. Xidian University; 2016.

    Google Scholar 

  9. Xing H, Xu Y. Neural network based on the detection of weak signals in chaotic background; 2007.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electronics and Information Engineering, Harbing Institute of Technology, Harbin, 150006, China

    ZhuXi Li, ZhenDong Yin & Jia Shi

Authors
  1. ZhuXi Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. ZhenDong Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jia Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to ZhenDong Yin .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA

    Qilian Liang

  2. School of Information and Communication Engineering, Dalian University of Technology, Dalian, China

    Xin Liu

  3. School of Information Science and Technology, Dalian Maritime University, Dalian, China

    Zhenyu Na

  4. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Wei Wang

  5. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Jiasong Mu

  6. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Baoju Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Li, Z., Yin, Z., Shi, J. (2020). PSO-RBF Small Target Detection in Sea Clutter Background. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-13-6504-1_124

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-6504-1_124

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6503-4

  • Online ISBN: 978-981-13-6504-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation