Evrişimli Sinir Ağı Kullanarak Dengesiz Doppler Radar Verisinde Hedef Tespiti

Yıl 2024, Cilt: 27 Sayı: 4, 1317 - 1326, 25.09.2024

Muhammed Erdoğan , Oktay Yıldız

https://doi.org/10.2339/politeknik.1180081

Öz

Kritik öneme sahip askeri ve sivil yerleşkelerin korunması geçmişte olduğu gibi günümüzde önemini korumaktadır. Bu amaçla, çeşitli sensörler barındıran sistemler geliştirilmektedir. Sensörlerin sağladığı verilerden bilginin elde edilmesi donanımların en verimli şekilde kullanılması açısından önemlidir. Radar sistemleri keşif, gözetleme ve tespit amacıyla sıklıkla kullanılmaktadır. Radar ile tespit edilen nesnelerin sınıflandırılması amacıyla kural tabanlı ve makine öğrenmesi tabanlı yöntemler mevcuttur. Makine öğrenmesi tabanlı yaklaşımlarda uzman görüşüne gerek kalmadan zaman içerisinde hedef nesnenin karakteristik özellikleri model tarafından öğrenilir. Bu sebeple bu yöntemler kural tabanlı yöntemlere nazaran daha avantajlıdır. Gerçekleştirilen bu çalışmada, dengesiz Doppler Radarı verisi üzerinde İHA’ların diğer nesnelerden ayırt edilmesi amacıyla hedef sınıflandırması yapılmıştır. Deneysel çalışmalarda en yüksek başarım SMOTE kullanılarak dengeli hale getirilen veri setinde elde edilmiş, önerilen CNN modeli ile %99,99 doğruluğa ulaşılmıştır.

Anahtar Kelimeler

Radar, SMOTE, insansız hava aracı, evrişimli sinir ağları

Destekleyen Kurum

Aselsan Akademi

Target Detection in Unbalanced Doppler Radar Data Using Convolutional Neural Network

Öz

The protection of critically important military and civil settlements resumes its importance today as in the past. For this purpose, systems with various sensors are being developed. Extracting information from the data provided by the sensors is also important for the most efficient use of the hardware. Radar systems are frequently used for reconnaissance, surveillance and detection purposes. There are rule-based and machine learning-based methods for the classification of objects detected by radar. In machine learning-based approaches, the characteristics of the target object are learned by the model over time without the need for expert opinion. For this reason, these methods are more advantageous than rule-based methods. In this study, target classification was made on unstable Doppler Radar data in order to distinguish UAVs from other objects. In experimental studies, the highest performance was obtained in the data set balanced using SMOTE, and %99,99 accuracy was achieved with the proposed CNN model.

Anahtar Kelimeler

Radar, SMOTE, unmanned serial vehicle, convolutional neural networks

Kaynakça

• [1] B. Torvik, K. E. Olsen and H. Griffiths, "Classification of Birds and UAVs Based on Radar Polarimetry", IEEE Geoscience and Remote Sensing Letters, 13(9): 1305-1309, (2016).
• [2] A. Manno-Kovacs, E. Giusti, F. Berizzi and L. Kovács, "Image Based Robust Target Classification for Passive ISAR", IEEE Sensors Journal, 19: 268-276, (2019).
• [3] F. Luo, S. Poslad and E. Bodanese, "Human Activity Detection and Coarse Localization Outdoors Using Micro-Doppler Signatures", IEEE Sensors Journal, 19(18): 8079-8094, (2019).
• [4] A. Angelov, A. Robertson, R. Murray-Smith and F. Fioranelli, "Practical Classification of different moving targets using automotive radar and deep neural networks", IET Radar, Sonar & Navigation, 12(10): 1082-1089, (2018).
• [5] X. Mou, X. Chen, N. Su and J. Guan, "Motion classification for radar moving target via STFT and convolution neural network", The Journal of Engineering, 2019(19): 6287-6290, (2019).
• [6] B. Oh, X. Guo, F. Wan, K. Toh and Z. Lin, "Micro-Doppler Mini-UAV Classification Using Empirical-Mode Decomposition Features", IEEE Geoscience and Remote SensingLetters, 15(2): 227-231, (2018).
• [7] A. Huizing, M. Heiligers, B. Dekker, J. de Wit, L. Cifola and R. Harmanny, "Deep Learning for Classification of Mini-UAVs Using Micro-Doppler Spectrograms in Cognitive Radar", IEEE Aerospace and Electronic Systems Magazine, 34(11): 46-56, (2019).
• [8] F. Fioranelli, M. Ritchie, S. Z. Gürbüz and H. Griffiths, "Feature Diversity for Optimized Human Micro-Doppler Classification Using Multistatic Radar", IEEE Transactions on Aerospace And Electronic Systems, 53(2): 640-654, (2017).
• [9] Roldan, I., del‐Blanco, C., Duque de Quevedo, Á., Ibañez Urzaiz, F., Gismero Menoyo, J., Asensio López, A., Berjón, D., Jaureguizar, F. and García, N., “DopplerNet: a convolutional neural network for recognising targets in real scenarios using a persistent range–Doppler radar”, IET Radar, Sonar & Navigation, 14(4): 593-600, (2020).
• [10] K. Polat, "A Hybrid Approach to Parkinson Disease Classification Using Speech Signal: The Combination of SMOTE and Random Forests", 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT), İstanbul, 1-3 (2019).
• [11] W. Feng, W. Huang and W. Bao, "Imbalanced Hyperspectral Image Classification With an Adaptive Ensemble Method Basedon SMOTE and Rotation Forest With Differentiated Sampling Rates", IEEE Geoscience and Remote Sensing Letters, 16(12): 1879-1883, (2019).
• [12] J. Wei, Z. Lu, K. Qiu, P. Li and H. Sun, "Predicting Drug Risk Level from Adverse Drug Reactions Using SMOTE and Machine Learning Approaches", IEEE Access, 8: 185761-185775, (2020).
• [13] C. Will, P. Vaishnav, A. Chakraborty and A. Santra, "Human Target Detection, Tracking, and Classification Using 24-GHz FMCW Radar", IEEE Sensors Journal, 19(17): 7283-7299, (2019).
• [14] S. Chen, H. Wang, F. Xu and Y. Jin, "Target Classification Using the Deep Convolutional Networks for SAR Images", IEEE Transactions on Geoscience And Remote Sensing, 54(8): 4806-4817, (2016).
• [15] H. Zhu, W. Wang and R. Leung, "SAR Target Classification Based on Radar Image Luminance Analysis by Deep Learning", IEEE Sensors Letters, 4(3): 1-4, (2020).
• [16] J. Wang, T. Zheng, P. Leia and X. Bai, "Ground Target Classification in Noisy SAR Images Using Convolutional Neural Networks", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(11): 4180-4192, (2018).
• [17] S. Chen, H. Wang, F. Xu and Y. -Q. Jin, "Target Classification Using the Deep Convolutional Networks for SAR Images", IEEE Transactions on Geoscience and Remote Sensing, 54(8): 4806-4817, (2016).
• [18] İ. Türkoğlu ve A. Arslan, "Darbeli radarlarda hedef sınıflama için AR modelinin güç spektrumu ve yapay sinir ağı temelli özellik çıkarma yöntemi", Politeknik Dergisi, 5: 121-127, (2002).
• [19] S. Özden, M. Dursun, A. Aksöz ve A. Saygın, "Prediction and Modelling of Energy Consumption on Temperature Control for Greenhouses", Politeknik Dergisi, 22: 213-217, (2019).
• [20] M. Çalışan, M. Talu, "Comparison of methods for determining activity from physical movements," Politeknik Dergisi, 24: 17-23, (2021).