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Identifying an aircraft from unknown-target information using radar is an important mission for national security to decide military strategy. The fighter aircraft is primarily designed to air combat missions, which can inflict severe damage and is an important factor in a tactical advantage. Analyzing unknown-target information via radar system requires highly experienced experts and military intelligence to identify fighter aircraft types. However, training experts to analyze data accurately can be time-consuming and costly. Therefore, the fighter aircraft classification requires modern approaches to comply with smart weapon systems for learning complex information to support the decision-making expert and solve the shortage of human resources. This paper generates the classification model of fighter aircraft based on unknown-target information using Feed-forward Back-propagation Neural Networks to learn complex information. Besides, this concept solves the shortage of skilled personnel. The created model is efficient and flexible, with learning and testing data with 95.38% and 86.30% accuracy, respectively.
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