Evrişimli Sinir Ağı Kullanarak Dengesiz Doppler Radar Verisinde Hedef Tespiti

Year 2024, , 1317 - 1326, 25.09.2024

Muhammed Erdoğan , Oktay Yıldız

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

Abstract

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.

Keywords

Radar, SMOTE, insansız hava aracı, evrişimli sinir ağları

Supporting Institution

Aselsan Akademi

Target Detection in Unbalanced Doppler Radar Data Using Convolutional Neural Network

Abstract

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.

Keywords

Radar, SMOTE, unmanned serial vehicle, convolutional neural networks

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