Skip to main content

Radar Emitter Identification in Multistatic Radar System: A Review

  • Conference paper
  • First Online:
Advances in Automation, Signal Processing, Instrumentation, and Control (i-CASIC 2020)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 700))

Abstract

Due to the increasing complexity of modern multi-functional radars in the electromagnetic environment, it is a challenging task to classify and identify the presence of different radar emitters. The presence of multiple number of active transmitters in the multistatic radar system makes radar emitter identification a big data problem as all are emitting dense complex signals in the electronic reconnaissance field. In order to classify and identify the radar emitters accurately and rapidly many researchers proposed numerous algorithms. To determine the radar emitter identification methods developed, this paper reviews various methods and techniques through a literature survey and classification of articles (collected from the online database) has been made from various algorithms and methods point of view.

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

Access this chapter

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
  • 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. Zhang C, Han Y, Zhang P, Song G, Zhou C (2019) Research on modern radar emitter modelling technique under complex electromagnetic environment. J Eng 20:7134–7138

    Article  Google Scholar 

  2. Li X, Huang Z, Wang F, Wang X, Liu T (2018) Toward convolutional neural networks on pulse repetition interval modulation recognition. IEEE Commun Lett 22(11):2286–2289

    Article  Google Scholar 

  3. Ray PS (1998) A novel pulse TOA analysis technique for radar identification. IEEE Trans Aerosp Electron Syst 34(3):716–721

    Article  Google Scholar 

  4. Nishiguchi KI, Kobayashi M (2000) Improved algorithm for estimating pulse repetition intervals. IEEE Trans Aerosp Electron Syst 36(2):407–421

    Article  Google Scholar 

  5. Logothetis A, Krishnamurthy V (1998) An interval-amplitude algorithm for deinterleaving stochastic pulse train sources. IEEE Trans Signal Processing 46(5):1344–1350

    Article  Google Scholar 

  6. Pu Y, Jin Z, Hu L (2008) A DOA-Based Separability Test and Confidence Evaluation Approach for Deinterleaving Pulse Sequence. Proceedings of Pacific-Asia Workshop on Computational Intelligence and Industrial Application (PACIIA) 2:954–957

    Google Scholar 

  7. Mao Y, Jun H, Guo G , Qing X (2009) An improved algorithm of PRI transform. In: WRI global congress on intelligent systems 3:145–149

    Google Scholar 

  8. Kawalec A, Rapacki T, Wnuczek S, Dudczyk J (2006) Mixed method based on intra pulse data and radiated emission to emitter sources recognition. In: International conference on microwaves, radar and wireless communications, pp 487–490

    Google Scholar 

  9. Ye H, Zheng l, Jiang W (2012) Comparison of unintentional frequency and phase modulation features for specific emitter identification. Electron lett 48:875–877

    Google Scholar 

  10. Zhang G, Laizhao H, Jin W (2003) Complexity feature extraction of radar emitter signals. In: Proceedings of Asia-Pacific conference on environmental electromagnetics, pp 495–498

    Google Scholar 

  11. Zhang HN, Rong LZ, Hu WD, Jin (2004) Entropy feature extraction approach of radar emitter signals. In: Proceedings of international conference on intelligent mechatronics and automation, pp 621–625

    Google Scholar 

  12. Huang G, Yang L (2004) Radar signal sorting based on blind signal extraction. In: Proceedings of international conference on signal processing, vol 3, pp 2120–2123

    Google Scholar 

  13. Kawalec A, Owczarek R (2006) Karhunen-Loeve transformation in radar signal features processing. In: Proceedings of international conference on microwaves, radar and wireless communications, pp 1168–1171

    Google Scholar 

  14. Dash D, Valarmathi J (2018) Multistatic radar emitter identification using entropy maximization based independent component analysis. J Eng Sci Technol 13(10):3238–3251

    Google Scholar 

  15. Jennison B, Brian K (2003) Detection of polyphase pulse compression waveforms using the Radon-ambiguity transform. IEEE Trans Aerosp Electron Syst 39(4):335–343

    Article  Google Scholar 

  16. Dudczyk J, Kawalec A (2013) Identification of emitter sources in the aspect of their fractal features. Bull Polish Acad Sci Tech 61:623–628

    Article  Google Scholar 

  17. Zhang G, Laizhao H, Weidong J (2004) A novel approach for radar emitter signal recognition. In: Proceedings of the Asia-Pacific conference on circuits and systems, vol 2, pp 817–820

    Google Scholar 

  18. Zhang G, Weidong JH (2004) Radar emitter signal recognition based on support vector machines. In: Proceedings of control, automation, robotics and vision, vol 2, pp 826–831

    Google Scholar 

  19. Ren M, Zhu Y, Mao Y, Han J (2007) Radar emitter signals classification using kernel principle component analysis and fuzzy support vector machines. In: Proceedings of international conference on wavelet analysis and pattern recognition, vol 3, pp 1442–1446

    Google Scholar 

  20. Swiercz E (2011) Automatic classification of LFM signals for radar emitter recognition using wavelet decomposition and LVQ classifier. Acta Phys Pol A 119:488–494

    Article  Google Scholar 

  21. Camastra F, Alessandro V (2005) A novel kernel method for clustering. IEEE Trans Pattern Anal Mach Intell 27(9):801–805

    Article  Google Scholar 

  22. Noone G (1995) Radar pulse train parameter estimation and tracking using neural networks. In: Proceedings of the international two-stream conference on artificial neural networks and expert systems, pp 95–98

    Google Scholar 

  23. Granger E, Savaria Y, Lavoie P (1998) A comparison of self-organizing neural networks for fast clustering of radar pulses. Signal Processing 64(3):249–269

    Article  Google Scholar 

  24. Hassan S, Bhatti AI, Latif A (2005) Emitter recognition using fuzzy adaptation of ARTMAP neural networks, Multitopic Conference, pp 1–6

    Google Scholar 

  25. Shieh C, Lin C (2002) A vector neural network for emitter identification. IEEE Trans Antennas Propag 50(8):1120–1127

    Article  Google Scholar 

  26. Liu H, Zhongmin L, Jiang W (2010) Incremental learning approach based on vector neural network for emitter identification. IET Signal Proc 4(1):45–54

    Article  Google Scholar 

  27. Granger E, Rubin S, Lavoie P (2001) A what-and-where fusion neural network for recognition and tracking of multiple radar emitters. Neural Networks 14(3):325–344

    Article  Google Scholar 

  28. Zhi-fu Y, Jun L, Kai L (2012) Radar emitter recognition based on PSO-BP network. AASRI Procedia 1:213–219

    Article  Google Scholar 

  29. Wnuk M, Kawalec A, Dudczyk J (2006) The method of regression analysis approach to the specific emitter identification. In: Proceedings of the international conference on microwaves, radar and wireless communications, pp 491–494

    Google Scholar 

  30. Xu D, Bo Y, Jiang W, Zhou Y (2008) An improved SVDU-IKPCA algorithm for specific emitter identification. In: Proceedings of the IEEE international conference on information and automation, pp 692–696

    Google Scholar 

  31. Li L, Ji H (2011) Radar emitter recognition based on cyclostationary signatures and sequential iterative least-square estimation. Expert Syst Appl 38:2140–2147

    Google Scholar 

  32. Hassan S, Bhatti I, Latif A (2005) Emitter recognition using fuzzy inference system. Proceedings of the symposium on emerging technologies, pp 204–208

    Google Scholar 

  33. Guan X, Xiao Y A novel emitter recognition approach to incomplete information system. In: Proceedings of international conference on machine learning and cybernetics, pp 1271–1275

    Google Scholar 

  34. Xin G, Xiao Y (2007) Discretization of continuous interval-valued attributes in rough set theory and its application. Proc Int Conf Mach Learn Cybern 7:3682–3686

    Google Scholar 

  35. Qiang G, Zhang X, Jing Z (2010) Study on emitter signal recognition based on rough sets and grey association theory. In: Proceedings of the international conference on signal processing proceedings, pp 2336–2340

    Google Scholar 

  36. Chen X, Yang H, Tang M (2012) A probabilistic fuzzy method for emitter identification based on genetic algorithm. In: Proceedings of the IEEE international conference information fusion, pp 635–640

    Google Scholar 

  37. Xu-bo L, Gui-Hu G (2012) The application and improvement of Grey associated analysis theory in Radar Emitter Source signal's sorting and Identification. In: Proceedings of the global symposium on millimeter waves, pp 441–444

    Google Scholar 

  38. Wang L, Ji HB, Shi Y (2011) Feature optimization of ambiguity function for radar emitter recognition. J Infrared Millimeter Waves 30(1):74–79

    Google Scholar 

  39. Yang LB, Zhang SS, Xiao B. (2013) Radar emitter signal recognition based on time-frequency analysis. In: Proceedings of the IET international radar conference IET international, pp 1–4

    Google Scholar 

  40. Zhu J, Zhao Y, Tang J (2013) Automatic recognition of radar signals based on time-frequency image character. Defence Sci J 3(1)308–315

    Google Scholar 

  41. Dash D, Jayaraman VA (2020) A probabilistic model for sensor fusion using range-only measurements in multistatic radar, IEEE Sensors Letters 4(6) pp 1-4

    Google Scholar 

  42. Ren M, Cai J, Zhu Y, He M (2008) Radar emitter signal classification based on mutual information and fuzzy support vector machines. In: Proceedings of the IEEE international conference on signal processing, pp 1641–1646

    Google Scholar 

  43. Zhou Y, Lee JP (1999) A MDL approach for determining the number of emitters using intra-pulse information, In: Proceedings of the IEEE pacific rim conference on communications, computers and signal processing, pp 548–551

    Google Scholar 

  44. Ren MQ, Zhu YQ, Mao Y, Han J (2007) Radar emitter signals classification using kernel principle component analysis and fuzzy support vector machines. In Proceedings of the IEEE international conference on wavelet analysis and pattern recognition, vol 3, pp 1442–1446

    Google Scholar 

  45. Lee DW, Han JW, Song KH, Lee WD (2008) A kernel density window clustering algorithm for radar pulses. In: Proceedings of the International Conference on Convergence and Hybrid Information Technology, vol 1, pp 1048–1053

    Google Scholar 

  46. Hassan SA, Bhatti I, Latif A (2005) Emitter recognition using fuzzy inference system. In: Proceedings of the IEEE Symposium on Emerging Technologies, pp 204–208

    Google Scholar 

  47. Chen X, Hu W, Yang H, Tang M (2012) A probabilistic fuzzy method for emitter identification based on genetic algorithm. In: Proceedings of the IEEE international conference information fusion, pp 635–640

    Google Scholar 

  48. Chen X, Hu WD (2012) Approach based on interval type-2 fuzzy logic system for emitter identification, Electronics lett 48(18)1156–1158.

    Google Scholar 

  49. Li L, Ji H (2009) Combining multiple SVM classifiers for radar emitter recognition. In: Proceedings of the international conference on fuzzy systems and knowledge discovery. vol 1 2009

    Google Scholar 

  50. Zhang M, Gao DM, Liu L (2017) Neural Networks for Radar Waveform Recognition, Symmetry 9(5):75–92

    Google Scholar 

  51. Chen YM, Lin CM, Hsueh CS (2014) Emitter identification of electronic intelligence system using type-2 fuzzy classifier. Syst Sci Control Eng 2(1):389–397

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dillip Dash .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Dash, D., Valarmathi, J. (2021). Radar Emitter Identification in Multistatic Radar System: A Review. In: Komanapalli, V.L.N., Sivakumaran, N., Hampannavar, S. (eds) Advances in Automation, Signal Processing, Instrumentation, and Control. i-CASIC 2020. Lecture Notes in Electrical Engineering, vol 700. Springer, Singapore. https://doi.org/10.1007/978-981-15-8221-9_248

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-8221-9_248

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-8220-2

  • Online ISBN: 978-981-15-8221-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics