Skip to main content
Springer Nature Link
Log in

Inferring the Number of Clusters for Radar Emitters via Threshold Segmentation and Information Fusion

  • Conference paper
  • First Online:
Wireless Artificial Intelligent Computing Systems and Applications (WASA 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14997))

  • 231 Accesses

Abstract

Effective clustering of radar emitters can enhance the perception capability of electronic warfare units in the electromagnetic environment. For the problem of radar emitter clustering with an unknown number of classes, this work proposes a method based on threshold segmentation and information fusion for inferring the number of clusters. Firstly, the radar emitter data is preprocessed to obtain the time-frequency information and spectrum information. Then, the threshold segmentation-based key time-frequency information processing algorithm is applied to process the time-frequency information. Next, the obtained key time-frequency information is fused with the spectrum information. Finally, principal component analysis and weighted consensus clustering are used to infer the number of clusters for radar emitters. The proposed method can accurately infer the number of clusters when the signal-to-noise ratio is not less than −4 dB. Experimental results demonstrate that the proposed key time-frequency information processing algorithm and information fusion algorithm can effectively improve the performance of the proposed method, and the performance of the proposed method is superior to traditional methods.

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

Similar content being viewed by others

References

  1. Mousavi, S.M., Zhu, W., Ellsworth, W., Beroza, G.: Unsupervised clustering of seismic signals using deep convolutional autoencoders. IEEE Geosci. Remote Sens. Lett. 16(11), 1693–1697 (2019)

    Article  Google Scholar 

  2. Wu, Z., Cao, W., Bi, D., Pan, J.: CLIPC: contrastive learning-based radar signal intra-pulse clustering. IEEE Internet Things J. (2023)

    Google Scholar 

  3. Wu, Z.L., Huang, X.X., Du, M., Xu, X.S., Bi, D., Pan, J.F.: Intra-pulse recognition of radar signals via bicubic interpolation WVD. IEEE Trans. Aerosp. Electron. Syst. (2023)

    Google Scholar 

  4. Clancy, T.C., Khawar, A., Newman, T.R.: Robust signal classification using unsupervised learning. IEEE Trans. Wirel. Commun. 10(4), 1289–1299 (2011)

    Article  Google Scholar 

  5. Scherreik, M., Rigling, B.: Online estimation of radar emitter cardinality via Bayesian nonparametric clustering. IEEE Trans. Aerosp. Electron. Syst. 57(6), 3791–3800 (2021)

    Article  Google Scholar 

  6. Lang, P., Fu, X., Cui, Z., Feng, C., Chang, J.: Subspace decomposition based adaptive density peak clustering for radar signals sorting. IEEE Sig. Process. Lett. 29, 424–428 (2021)

    Article  Google Scholar 

  7. Gasperini, S., Paschali, M., Hopke, C., Wittmann, D., Navab, N.: Signal clustering with class-independent segmentation. In: 2020 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3982–3986. IEEE, Virtual Conference (2020)

    Google Scholar 

  8. Cheng, Y.: Mean shift, mode seeking, and clustering. IEEE Trans. Pattern Anal. Mach. Intell. 17(8), 790–799 (1995)

    Article  Google Scholar 

  9. Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pp. 226–231. AAAI Press, Portland, Oregon (1996)

    Google Scholar 

  10. Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH: an efficient data clustering method for very large databases. ACM SIGMOD Rec. 25(2), 103–114 (1996)

    Article  Google Scholar 

  11. Ankerst, M., Breunig, M.M., Kriegel, H.P., Sander, J.: OPTICS: ordering points to identify the clustering structure. ACM SIGMOD Rec. 28(2), 49–60 (1999)

    Article  Google Scholar 

  12. Frey, B. J., Dueck, D.: Clustering by passing messages between data points. Science 315(5814), 972–976 (2007)

    Google Scholar 

  13. Ünlü, R., Xanthopoulos, P.: Estimating the number of clusters in a dataset via consensus clustering. Expert Syst. Appl. 125, 33–39 (2019)

    Article  Google Scholar 

  14. Thorndike, R.L.: Who belongs in the family? Psychometrika 18(4), 267–276 (1953)

    Article  Google Scholar 

  15. Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)

    Article  Google Scholar 

  16. Caliński, T., Harabasz, J.: A dendrite method for cluster analysis. Commun. Stat. Theory Methods 3(1), 1–27 (1974)

    Article  MathSciNet  Google Scholar 

  17. Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. 2, 224–227 (1979)

    Article  Google Scholar 

  18. Zhou, S., Xu, Z., Liu, F.: Method for determining the optimal number of clusters based on agglomerative hierarchical clustering. IEEE Trans. Neural Netw. Learn. Syst. 28(12), 3007–3017 (2016)

    Article  MathSciNet  Google Scholar 

  19. Kingrani, S.K., Levene, M., Zhang, D.: Estimating the number of clusters using diversity. Artif. Intell. Res. 7(1), 15–22 (2018)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electronic Countermeasures, National University of Defense Technology, Hefei, 230031, Anhui, China

    Zilong Wu, Daping Bi & Jifei Pan

Authors
  1. Zilong Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daping Bi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jifei Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jifei Pan .

Editor information

Editors and Affiliations

  1. Georgia State University, Atlanta, GA, USA

    Zhipeng Cai

  2. Old Dominion University, Norfolk, VA, USA

    Daniel Takabi

  3. Beijing University of Posts and Telecommunications, Beijing, China

    Shaoyong Guo

  4. Shandong University, Qingdao, China

    Yifei Zou

Rights and permissions

Reprints and permissions

Copyright information

© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wu, Z., Bi, D., Pan, J. (2025). Inferring the Number of Clusters for Radar Emitters via Threshold Segmentation and Information Fusion. In: Cai, Z., Takabi, D., Guo, S., Zou, Y. (eds) Wireless Artificial Intelligent Computing Systems and Applications. WASA 2024. Lecture Notes in Computer Science, vol 14997. Springer, Cham. https://doi.org/10.1007/978-3-031-71464-1_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-71464-1_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-71463-4

  • Online ISBN: 978-3-031-71464-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics