Aerial situation threat mining based on deep clustering algorithm

DOI:	10.11809/bqzbgcxb2024.11.015
Keywords:	aerial situation; threat analysis; deep clustering; threat attribute indicators; threat level
Abstract:	To address the issues of high subjectivity and low accuracy in commanders’ analysis of air target threats, this paper proposes an air target threat mining and clustering model based on deep clustering algorithms.The model first establishes a comprehensive air situation threat mining index system, framing the target threat level classification as an optimal clustering problem, thus enabling in depth threat analysis.By inputting real data of air situation threat factors, which has been preprocessed, the model employs deep clustering algorithms for simulation experiments.Experimental results demonstrate the model’s superior clustering performance, effectively categorizing large targets such as bombers into distinct threat levels, separate from refueling and early warning aircraft.The model successfully fulfills the task of threat mining, providing robust theoretical support for commanders to accurately and swiftly analyze air threats and make informed decisions.
Published:	2024-11-30
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