A framework for multi-mode radars discrimination based on extended PDW

Document Type : Original Article

Authors

1 Doctoral student, Imam Hossein University, Tehran, Iran

2 Associate Professor, Noshirvani University of Technology, Babol, Iran

3 Assistant Professor, Imam Hossein University (AS), Tehran, Iran

Abstract

Due to developments have been occurred in ECCM techniques of radars, pulse separation methods in ESM systems rely on intera-pulse modulation instead of analyzing pulse descriptive words in traditional methods. Extraction of pulse modulation is a suitable method but in the case of multi-mode radars, the number of targets is overestimated by changing the intra-pulse modulation. The purpose of this paper is to detect multi-mode radars with various types of internal modulation in a dense radar environment. The proposed solution is to add multi-mode radars detection to the existing pulse separation methods at the post processing stage. This method involves providing an appropriate framework for examining the separated pulse string by defining and selecting similarity criteria from the extended PDW. In this method, at first the distinguishing features of each radar are extracted and the similarity criteria of each feature are calculated to check the similarity between the two pulse strings. Input data contains information separated from real radars received by ESM system. Due to the uncertainties of each criteria, similarity scores are computed through the fuzzy roles and normalization and training dataset is formed. The data table is then used to train a perceptron neural network. The trained network can detect multimode radars automatically. To test the network, a section of the data table is applied to the network and trained network succeed in 100% of test data to distinguish multi-mode radars from the distinct radars.

Keywords

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References

[1]    J. Dudczyk, “Radar Emission Sources Identification Based on Hierarchical Agglomerative Clustering for Large Data Sets,” Journal of Sensors, vol. 2016, pp. 1879327, 2016.
[2]    M. A. M. j. Ghalandari , E.   Morady, “Nearest neighbor method with priority selection of denser clusters by analysis of similarity matrix for radar pulse clustering,” Marine Technology, vol. 4, no. 4, pp. 13-24, 2016. (in Persian)
[3]     K. Gençol, A. Kara, and N. At, “Improvements on deinterleaving of radar pulses in dynamically varying signal environments,” Digital Signal Processing, vol. 69, pp. 86-93, 2017.
[4]    N. G. a. P. S. Vimala, “Generation and Deinterleaving of Radar Signals in Electronic Warfare Environment using SDIF Histogram Algorithm,” International Journal of Scientific Research and Reviews, vol. 7, pp. 10, 2018.
[5]    F. Paisana, N. J. Kaminski, N. Marchetti, & L. A. DaSilva, “Signal Processing for Temporal Spectrum Sharing in a Multi-Radar Environment,” IEEE Transactions on Cognitive Communications and Networking, vol. 3, no. 2, pp. 123-137, 2017.
[6]    E. Norgren, "Pulse repetition interval modulation classification using machine learning," 2019.
[7]    Y. Liu, & Q. Zhang, “Improved method for deinterleaving radar signals and estimating PRI values,” IET Radar, Sonar & Navigation, vol. 12, no. 5, pp. 506-514, 2018.
[8]    A.-L. He, D.-G. Zeng, J. Wang, & B. Tang, “Multi-parameter signal sorting algorithm based on dynamic distance clustering,” Journal of Electronic Science and Technology, vol. 7, no. 3, pp. 249-253, 2009.
[9]    Z.-M. Liu, & S. Y. Philip, “Classification, denoising, and deinterleaving of pulse streams with recurrent neural networks,” IEEE Transactions on Aerospace and Electronic Systems, vol. 55, no. 4, pp. 1624-1639, 2018.
[10]    S. Wei, Q. Qu, H. Su, J. Shi, X. Zeng, & X. Hao, “Intra-pulse modulation radar signal recognition based on Squeeze-and-Excitation networks,” Signal, Image and Video Processing, pp. 1-9, 2020.
[11]  Y. Huang, W. Jin, B. Li, P. Ge, & Y. Wu, “Automatic modulation recognition of radar signals based on manhattan distance-based features,” IEEE Access, vol. 7, pp. 41193-41204, 2019.
[12]  X. Liao, B. Li, & B. Yang, “A Novel Classification and Identification Scheme of Emitter Signals Based on Ward’s Clustering and Probabilistic Neural Networks with Correlation Analysis,” Computational intelligence and neuroscience, vol. 2018, 2018.
[13]  T. R. Kishore, & K. D. Rao, “Automatic intrapulse modulation classification of advanced LPI radar waveforms,” IEEE Transactions on Aerospace and Electronic Systems, vol. 53, no. 2, pp. 901-914, 2017.
[14]  S. Dai, W. Lei, Y. Cheng, & D. Wang, “Clustering of DOA data in radar pulse based on SOFM and CDbw,” Journal of Electronics (China), vol. 31, no. 2, pp. 107-114, 2014.
[15]  Y. Sun, J. Li, F. Lin, & G. Pan, "Automatic Signal Modulation Recognition based on Deep Convolutional Neural Network." pp. 550-554, 2021.
[16]  R. A. S. P. Bayat, M. M. Nayebi, “The Deinterleaving of Radar Pulses in Interception systems,” Toosi University of Technology, 2010. (in Persian)
[17]  G. Revillon, A. Mohammad-Djafari, & C. Enderli, “Radar emitters classification and clustering with a scale mixture of normal distributions,” IET Radar, Sonar & Navigation, vol. 13, no. 1, pp. 128-138, 2018.
[18]  Z. Wu, Z. Yang, H. Sun, Z. Yin, & A. Nallanathan, “Hybrid radar emitter recognition based on rough k-means classifier and SVM,” EURASIP Journal on Advances in Signal Processing, vol. 2012, no. 1, pp. 1-9, 2012.
[19]  G. Zhang, "Intra-pulse modulation recognition of advanced radar emitter signals using intelligent recognition method." pp. 707-712, 2006.
[20]  Q. Guo, P. Nan, & J. Wan, “Signal classification method based on data mining for multi-mode radar,” Journal of Systems Engineering and Electronics, vol. 27, no. 5, pp. 1010-1017, 2016.
[21]  J. Wan, P. Nan, Q. Guo, & Q. Wang, “Multi-mode radar signal sorting by means of spatial data mining,” Journal of Communications and Networks, vol. 18, no. 5, pp. 725-734, 2016.
[22]  S. Q. Wang, J. Bai, X. Y. Huang, C. Y. Gao, & P. F. Wan, “Analysis of radar emitter signal sorting and recognition model structure,” Procedia Computer Science, vol. 154, pp. 500-503, 2019.
[23]  G. Revillon, “Uncertainty in radar emitter classification and clustering,” 2019.
[24]  Q. Guo, L. Teng, L. Qi, X. Ji, & J. Xiang, “A Novel Radar Signals Sorting Method-Based Trajectory Features,” IEEE Access, vol. 7, pp. 171235-171245, 2019.
[25]  R. Wiley, ELINT: The interception and analysis of radar signals: Artech, 2006.
[26]  M. Li, M. He, J. Han, & Y. Tang, "A new clustering and sorting algorithm for radar emitter signals." p. 012009, 2020.
Radar
Volume 9, Issue 2 - Serial Number 26
November 2022
Pages 107-117

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History

  • Receive Date: 13 February 2022
  • Revise Date: 08 April 2022
  • Accept Date: 01 October 2022
  • Publish Date: 22 November 2022

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