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Kümeleme ve PRI Tip Tanımaya Dayalı Yeni Bir Ayrıştırma Yaklaşımı

Year 2025, EARLY VIEW, 1 - 1

Şefika Çağlan , Ali Değirmenci , İlyas Çankaya

https://doi.org/10.2339/politeknik.1489107

Abstract

Bir elektronik savaş ortamında, her biri belirli görevleri yerine getirmek için özel olarak tasarlanmış farklı sinyal dalga formlarına sahip çok sayıda radar çalışır. Radarların ayrıştırılması bir elektronik harp sisteminin temel ve önemli bir işlevidir. Ayrıştırma işleminin ardından Darbe Tekrarlama Aralığı (DTA) modülasyon tipinin belirlenmesi, radarın tanınması ve işlevinin anlaşılması için çok önemlidir. Bu çalışmada, radar darbelerini ayrıştırmak ve DTA modülasyonunu tanımak için yeni bir kümeleme ve kural tabanlı yöntem önerilmiştir. Radar sinyalini kümelemek için Kümeleme Yapısını Tanımlamak için Noktaları Sıralama (OPTICS) yöntemi uygulanmıştır. Kümelenmiş verilerdeki DTA'yı bulmak için darbelerin geliş zamanı farkı kullanılmıştır. Geliş zamanı farkı temelli yöntem sonucunda elde edilen DTA değerlerinden DTA tipini belirlemek için kural tabanlı bir yöntem kullanılmıştır. Deneylerde, kümeleme ve DTA tipi tanıma aşamaları ayrı ayrı analiz edilmiştir. OPTICS'in performansı (i) yüksek küme sayıları, (ii) kümelerin yakınlığı, (iii) farklı küme yoğunlukları ve formları gibi farklı koşullar altında test edilmiş ve kümelemede iyi sonuçlar vermiştir. DTA tipi bulma performansı da 4 farklı DTA türünden (sabit, çevik, kademeli ve bekle&değiştir) oluşan bir simülasyon veri kümesi üzerinde test edilmiştir. Sonuçlar, yeni yöntemin bir radar sinyalinin PRI türlerini belirlemede etkili olduğunu göstermektedir.

Keywords

ayrıştırma kümeleme OPTICS darbe tekrarlama aralığı modülasyonu elektronik harp

Ethical Statement

Bu makalenin yazar(lar)ı çalışmalarında kullandıkları materyal ve yöntemlerin etik kurul izni ve/veya yasal-özel bir izin gerektirmediğini beyan ederler.

References

  • [1] https://falcon.blu3wolf.com/Docs/Electronic-Warfare-Fundamentals.pdf , “Electronic Warfare Fundamentals”, (2000).
  • [2] Wiley R.G., “Electronic Intelligence: The Analysis of Radar Signals”, 0-89006-592-6, Artech House, 2nd Edition, (1993).
  • [3] Kauppi, J-P., Martikainen, K.S., “An Efficient Set of Features For Pulse Repetition Interval Modulation Recognition”, 2007 IET International Conference on Radar Systems, Edinburgh, UK, (2007).
  • [4] Han J., Park C.H., “A Unified Method for Deinterleaving and PRI Modulation Recognition of Radar Pulses Based on Deep Neural Networks”, IEEE Access, 9: 89360-89375, (2021).
  • [5] Cheng W., Zhang Q., Dong J., Wang C., Liu X., Fang G., “An Enhanced Algorithm for Deinterleaving Mixed Radar Signals”, IEEE Transactions on Aerospace and Electronic Systems, 57: 3927-3940, (2021).
  • [6] Mottier M., Chardon G., Pascal F., “Deinterleaving and Clustering Unknown Radar Pulses”, 2021 IEEE Radar Conference (RadarConf21), 1-6, (2021).
  • [7] Hasani H., Khosravi M. R., “Pulse Deinterleaving Based On Fusing PDWs and PRI Extraction Process For Radar‑Assisted Edge Devices Considering Computational Costs”, Eurosip Journal on Wireless Communications and Networking, 98: 1-14, (2021).
  • [8] Kang S., Yuan S., Wang C., Liu Z., “Sequential Extraction and Recognition of Pulse Group Structure For Multi‐Function Radar”, IET The Institution of Engineering and Technology, 16: 678-691, (2022).
  • [9] Chao W., Weisong L., Xueqiong L., Xiang W., Zhitao H., “A New Radar Signal Multiparameter-Based Deinterleaving Method”, Cornell University - Electrical Engineering and Systems Science, (2022).
  • [10] Chao W., Liting S., Zhangmeng L., Zhitao H., “A Radar Signal Deinterleaving Method Based on Semantic Segmentation with Neural Network”, IEEE Transactions on Signal Processing, 70: 5806-5821, (2022).
  • [11] Xie M., Zhao C., Zhao Y., Hu D., Wang Z., “A Novel Method For Deinterleaving Radar Signals First‐Order Difference Curve Based On Sorted TOA Difference Sequence”, IET The Institution of Engineering and Technology, 17: 1-13, (2022).
  • [12] Feng H. C., Tang B., Wan T., “Radar Pulse Repetition Interval Modulation Recognition with Combined Net and Domain-Adaptive Few-Shot Learning”, Digital Signal Processing, 127: 1-11, (2022).
  • [13] Cheng W., Zhang Q., Dong J., Wang H., Liu X., “An Efficient Algorithm For De-Interleaving Staggered PRI Signals”, Applied Sciences, 13: 1-19, (2023).
  • [14] Mottier M., Chardon G., Pascal F., “Deinterleaving Radar Emitters With Optimal Transport Distances”, IEEE Transactions on Aerospace and Electronic Systems, 1-13, (2023).
  • [15] Nuhoglu M. A., Cirpan H. A., “Radar Signal Deinterleaving in Electronic Warfare Systems: A Combined Approach”, IEEE Access, 11: 142043-142061, (2023).
  • [16] Mao Y., Ren W., Li X., Yang Z., Cao W., “Sep-RefineNet A Deinterleaving Method for Radar Signals Based on Semantic Segmentation”, Applied Sciences, 13: 1-18, (2023).
  • [17] Estivill-Castro, V., “Why So Many Clustering Algorithms: A Position Paper”, ACM SIGKDD Explorations Newsletter, 4(1): 65-75, (2002).
  • [18] Oti, E. U., Olusola, M. O., Eze, F. C., Enogwe, S. U., “Comprehensive Review of K-means Clustering Algorithms”, International Journal of Advances in Scientific Research and Engineering, 7(8): 64-68, (2021).
  • [19] Divya, G., Babu, R. S. R., “Analysis of the Applicability Criterion For K means Clustering Algorithm Run Ten Number of Times On the First 25 Numbers of the Fibanocci Series”, EPRA International Journal of Research and Development (IJRD), 6(9): 206-213, (2021).
  • [20] MacQueen, J., “Some Methods For Classification and Analysis of Multivariate Observations”, Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1: 281-297, (1967).
  • [21] Yuan, C., Yang, H., “Research on K-value Selection Method of K-means Clustering Algorithm”, J-Multidisciplinary Scientific Journal, 2(2): 226-235, (2019).
  • [22] Ankerst M., Breunig M. M., Kriegel H., Sander J., “OPTICS: Ordering Points To Identify the Clustering Structure”, ACM Sigmod Record, 28: 49-60, (1999).
  • [23] Fan Y., Wang M., “Specification Mining Based on the Ordering Points to Identify the Clustering Structure Clustering Algorithm and Model Chacking”, Applied Sciences, 17: 1-18, (2024).
  • [24] Jain, A.K., Murty, M.N., Flynn, P.J., “Data Clustering: A Review”, ACM Computing Surveys (CSUR), 31(3): 264–323, (1999).
  • [25] Liao, T.W., “Clustering of Time Series Data-A Survey”, Pattern Recognition, 38(11): 1857–1874, (2005).
  • [26] Bose, I., Chen, X., “Detecting the Migration of Mobile Service Customers Using Fuzzy Clustering”, Information&Management, 52(2): 227–238, (2015).
  • [27] Xie, W-B., Lee, Y-L., Wang, C., Chen, D-B., Zhou, T., “Hierarchical Clustering Supported By Reciprocal Nearest Neighbors”, Information Sciences, 527: 279-292, (2020).
  • [28] Tekerek, A., Dörterler, M., “The Adaptation of Gray Wolf Optimizer to Data Clustering”, Journal of Polytechnic, 25(4): 1761-1767, (2022).
  • [29] Ibrahım M.H., “WBBA-KM: A Hybrid Weight-based Bat Algorithm With the K-means Algorithm for Cluster Analysis”, Journal of Polytechnic, 25(1): 65-73, (2022).
  • [30] Akalın F., Yumuşak N., “Classification of Exon and Intron Regions on DNA Sequences with Hybrid Use of SBERT and ANFIS Approaches”, Journal of Polytechnic, 27(3): 1043-1053, (2024).
  • [31] Ataş K., Kaya A., Myderrizi I., “Covid-19 Diagnosis From X-ray Images With Artificial Neural Network Based Model”, Journal of Polytechnic, 26(2): 541-551, (2023).
  • [32] Balcı F., Yılmaz S., “Faster R-CNN Structure for Computer Vision-based Road Pavement Distress Detection”, Journal of Polytechnic, 26(2): 701-710, (2023).
  • [33] Yin H., Aryani A., Petrie S., Nambissan A., Astudillo A., Cao S., “A Rapid Review of Clustering Algorithms”, Cornell University - Computer Science, (2024).
  • [34] Zhang, X., Sun, Y., Liu, H., Hou, Z., Zhao, F., Zhang, C., “Improved Clustering Algorithms for Image Segmentation Based on Non-Local Information and Back Projection”, Information Sciences, 550: 129-144, (2021).
  • [35] Jiang, D., Chen, G., Ooi, B. C., Tan, K-L., Wu, S., “epiC: An Extensible and Scalable System for Processing Big Data”, Proceedings of the VLDB Endowment, 7(7): 541-552, (2014).
  • [36] Dorai, C., Jain, A.K., “Shape Spectra Based View Grouping for Free-Form Objects”, International conference on image processing, 3: 340-343, (1995).

A New Deinterleaving Approach Based On Clustering and PRI Type Recognition

Year 2025, EARLY VIEW, 1 - 1

Şefika Çağlan , Ali Değirmenci , İlyas Çankaya

https://doi.org/10.2339/politeknik.1489107

Abstract

In an electronic warfare environment, numerous radars operate, each designed with distinct signal waveforms tailored to fulfill specific missions. The deinterleaving of radars is a fundamental function of an electronic warfare system. Following deinterleaving, identifying the Pulse Repetition Interval (PRI) modulation type becomes essential for enhanced radar recognition and understanding of its function. In this study, a new clustering and rule-based method is proposed to deinterleave radar pulses and recognize the PRI modulations. Ordering Points to Identify Clustering Structure (OPTICS) method is employed to cluster the radar signals. Difference of Time of Arrival (DTOA)-based method is employed to find the PRIs in the clustered data. A rule-based method is used to determine the PRI type from the obtained PRI values. In the experiments, the clustering and PRI type recognition phases were analyzed separately. The performance of OPTICS was tested under different conditions: (i) high cluster counts, (ii) close proximity of clusters, (iii) different cluster densities and forms, and showed good results in clustering. The PRI type detection performance was also tested on a simulation dataset consisting of 4 different PRI types (constant, agile, stagger and dwell&switch). The results indicate that the new method is effective in determining the PRI modulations.

Keywords

deinterleaving clustering OPTICS pulse repetition interval modulation electronic warfare

Ethical Statement

The author(s) of this article declare that the materials and methods used in this study do not require ethical committee permission and/or legal-special permission.

References

  • [1] https://falcon.blu3wolf.com/Docs/Electronic-Warfare-Fundamentals.pdf , “Electronic Warfare Fundamentals”, (2000).
  • [2] Wiley R.G., “Electronic Intelligence: The Analysis of Radar Signals”, 0-89006-592-6, Artech House, 2nd Edition, (1993).
  • [3] Kauppi, J-P., Martikainen, K.S., “An Efficient Set of Features For Pulse Repetition Interval Modulation Recognition”, 2007 IET International Conference on Radar Systems, Edinburgh, UK, (2007).
  • [4] Han J., Park C.H., “A Unified Method for Deinterleaving and PRI Modulation Recognition of Radar Pulses Based on Deep Neural Networks”, IEEE Access, 9: 89360-89375, (2021).
  • [5] Cheng W., Zhang Q., Dong J., Wang C., Liu X., Fang G., “An Enhanced Algorithm for Deinterleaving Mixed Radar Signals”, IEEE Transactions on Aerospace and Electronic Systems, 57: 3927-3940, (2021).
  • [6] Mottier M., Chardon G., Pascal F., “Deinterleaving and Clustering Unknown Radar Pulses”, 2021 IEEE Radar Conference (RadarConf21), 1-6, (2021).
  • [7] Hasani H., Khosravi M. R., “Pulse Deinterleaving Based On Fusing PDWs and PRI Extraction Process For Radar‑Assisted Edge Devices Considering Computational Costs”, Eurosip Journal on Wireless Communications and Networking, 98: 1-14, (2021).
  • [8] Kang S., Yuan S., Wang C., Liu Z., “Sequential Extraction and Recognition of Pulse Group Structure For Multi‐Function Radar”, IET The Institution of Engineering and Technology, 16: 678-691, (2022).
  • [9] Chao W., Weisong L., Xueqiong L., Xiang W., Zhitao H., “A New Radar Signal Multiparameter-Based Deinterleaving Method”, Cornell University - Electrical Engineering and Systems Science, (2022).
  • [10] Chao W., Liting S., Zhangmeng L., Zhitao H., “A Radar Signal Deinterleaving Method Based on Semantic Segmentation with Neural Network”, IEEE Transactions on Signal Processing, 70: 5806-5821, (2022).
  • [11] Xie M., Zhao C., Zhao Y., Hu D., Wang Z., “A Novel Method For Deinterleaving Radar Signals First‐Order Difference Curve Based On Sorted TOA Difference Sequence”, IET The Institution of Engineering and Technology, 17: 1-13, (2022).
  • [12] Feng H. C., Tang B., Wan T., “Radar Pulse Repetition Interval Modulation Recognition with Combined Net and Domain-Adaptive Few-Shot Learning”, Digital Signal Processing, 127: 1-11, (2022).
  • [13] Cheng W., Zhang Q., Dong J., Wang H., Liu X., “An Efficient Algorithm For De-Interleaving Staggered PRI Signals”, Applied Sciences, 13: 1-19, (2023).
  • [14] Mottier M., Chardon G., Pascal F., “Deinterleaving Radar Emitters With Optimal Transport Distances”, IEEE Transactions on Aerospace and Electronic Systems, 1-13, (2023).
  • [15] Nuhoglu M. A., Cirpan H. A., “Radar Signal Deinterleaving in Electronic Warfare Systems: A Combined Approach”, IEEE Access, 11: 142043-142061, (2023).
  • [16] Mao Y., Ren W., Li X., Yang Z., Cao W., “Sep-RefineNet A Deinterleaving Method for Radar Signals Based on Semantic Segmentation”, Applied Sciences, 13: 1-18, (2023).
  • [17] Estivill-Castro, V., “Why So Many Clustering Algorithms: A Position Paper”, ACM SIGKDD Explorations Newsletter, 4(1): 65-75, (2002).
  • [18] Oti, E. U., Olusola, M. O., Eze, F. C., Enogwe, S. U., “Comprehensive Review of K-means Clustering Algorithms”, International Journal of Advances in Scientific Research and Engineering, 7(8): 64-68, (2021).
  • [19] Divya, G., Babu, R. S. R., “Analysis of the Applicability Criterion For K means Clustering Algorithm Run Ten Number of Times On the First 25 Numbers of the Fibanocci Series”, EPRA International Journal of Research and Development (IJRD), 6(9): 206-213, (2021).
  • [20] MacQueen, J., “Some Methods For Classification and Analysis of Multivariate Observations”, Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1: 281-297, (1967).
  • [21] Yuan, C., Yang, H., “Research on K-value Selection Method of K-means Clustering Algorithm”, J-Multidisciplinary Scientific Journal, 2(2): 226-235, (2019).
  • [22] Ankerst M., Breunig M. M., Kriegel H., Sander J., “OPTICS: Ordering Points To Identify the Clustering Structure”, ACM Sigmod Record, 28: 49-60, (1999).
  • [23] Fan Y., Wang M., “Specification Mining Based on the Ordering Points to Identify the Clustering Structure Clustering Algorithm and Model Chacking”, Applied Sciences, 17: 1-18, (2024).
  • [24] Jain, A.K., Murty, M.N., Flynn, P.J., “Data Clustering: A Review”, ACM Computing Surveys (CSUR), 31(3): 264–323, (1999).
  • [25] Liao, T.W., “Clustering of Time Series Data-A Survey”, Pattern Recognition, 38(11): 1857–1874, (2005).
  • [26] Bose, I., Chen, X., “Detecting the Migration of Mobile Service Customers Using Fuzzy Clustering”, Information&Management, 52(2): 227–238, (2015).
  • [27] Xie, W-B., Lee, Y-L., Wang, C., Chen, D-B., Zhou, T., “Hierarchical Clustering Supported By Reciprocal Nearest Neighbors”, Information Sciences, 527: 279-292, (2020).
  • [28] Tekerek, A., Dörterler, M., “The Adaptation of Gray Wolf Optimizer to Data Clustering”, Journal of Polytechnic, 25(4): 1761-1767, (2022).
  • [29] Ibrahım M.H., “WBBA-KM: A Hybrid Weight-based Bat Algorithm With the K-means Algorithm for Cluster Analysis”, Journal of Polytechnic, 25(1): 65-73, (2022).
  • [30] Akalın F., Yumuşak N., “Classification of Exon and Intron Regions on DNA Sequences with Hybrid Use of SBERT and ANFIS Approaches”, Journal of Polytechnic, 27(3): 1043-1053, (2024).
  • [31] Ataş K., Kaya A., Myderrizi I., “Covid-19 Diagnosis From X-ray Images With Artificial Neural Network Based Model”, Journal of Polytechnic, 26(2): 541-551, (2023).
  • [32] Balcı F., Yılmaz S., “Faster R-CNN Structure for Computer Vision-based Road Pavement Distress Detection”, Journal of Polytechnic, 26(2): 701-710, (2023).
  • [33] Yin H., Aryani A., Petrie S., Nambissan A., Astudillo A., Cao S., “A Rapid Review of Clustering Algorithms”, Cornell University - Computer Science, (2024).
  • [34] Zhang, X., Sun, Y., Liu, H., Hou, Z., Zhao, F., Zhang, C., “Improved Clustering Algorithms for Image Segmentation Based on Non-Local Information and Back Projection”, Information Sciences, 550: 129-144, (2021).
  • [35] Jiang, D., Chen, G., Ooi, B. C., Tan, K-L., Wu, S., “epiC: An Extensible and Scalable System for Processing Big Data”, Proceedings of the VLDB Endowment, 7(7): 541-552, (2014).
  • [36] Dorai, C., Jain, A.K., “Shape Spectra Based View Grouping for Free-Form Objects”, International conference on image processing, 3: 340-343, (1995).
There are 36 citations in total.

Details

Primary Language English
Subjects Engineering Practice
Journal Section Research Article
Authors

Şefika Çağlan 0000-0001-9406-4109

Ali Değirmenci 0000-0001-9727-8559

İlyas Çankaya 0000-0002-6072-3097

Early Pub Date April 27, 2025
Publication Date
Submission Date May 25, 2024
Acceptance Date October 2, 2024
Published in Issue Year 2025 EARLY VIEW

Cite

APA Çağlan, Ş., Değirmenci, A., & Çankaya, İ. (2025). A New Deinterleaving Approach Based On Clustering and PRI Type Recognition. Politeknik Dergisi1-1. https://doi.org/10.2339/politeknik.1489107
AMA Çağlan Ş, Değirmenci A, Çankaya İ. A New Deinterleaving Approach Based On Clustering and PRI Type Recognition. Politeknik Dergisi. Published online April 1, 2025:1-1. doi:10.2339/politeknik.1489107
Chicago Çağlan, Şefika, Ali Değirmenci, and İlyas Çankaya. “A New Deinterleaving Approach Based On Clustering and PRI Type Recognition”. Politeknik Dergisi, April (April 2025), 1-1. https://doi.org/10.2339/politeknik.1489107.
EndNote Çağlan Ş, Değirmenci A, Çankaya İ (April 1, 2025) A New Deinterleaving Approach Based On Clustering and PRI Type Recognition. Politeknik Dergisi 1–1.
IEEE Ş. Çağlan, A. Değirmenci, and İ. Çankaya, “A New Deinterleaving Approach Based On Clustering and PRI Type Recognition”, Politeknik Dergisi, pp. 1–1, April 2025, doi: 10.2339/politeknik.1489107.
ISNAD Çağlan, Şefika et al. “A New Deinterleaving Approach Based On Clustering and PRI Type Recognition”. Politeknik Dergisi. April 2025. 1-1. https://doi.org/10.2339/politeknik.1489107.
JAMA Çağlan Ş, Değirmenci A, Çankaya İ. A New Deinterleaving Approach Based On Clustering and PRI Type Recognition. Politeknik Dergisi. 2025;:1–1.
MLA Çağlan, Şefika et al. “A New Deinterleaving Approach Based On Clustering and PRI Type Recognition”. Politeknik Dergisi, 2025, pp. 1-1, doi:10.2339/politeknik.1489107.
Vancouver Çağlan Ş, Değirmenci A, Çankaya İ. A New Deinterleaving Approach Based On Clustering and PRI Type Recognition. Politeknik Dergisi. 2025:1-.

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