| Citation: | MA Y T，SUN P，ZHANG J Y，et al. Aerial target automatic grouping method based on MDk-DPC[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（10）：3219-3229 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0797
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Air target grouping is a popular topic for research in the area of combat scenario assessment and can be thought of as essentially an uncountable class clustering issue. Aiming at the unknown air battlefield environment, a MDk-DPC algorithm based on manifold distance and k-nearest neighbor sampling density is proposed from the perspective of clustering. First, manifold distance is introduced to replace Euclidean distance to increase the similarity of objects in the same manifold. Secondly, the target's local density is determined using the k-nearest neighbors method, allowing the local density to more accurately represent the distribution surrounding the targets. Finally, an adaptive cluster center selection method is proposed to automatically determine cluster centers, and the DPC algorithm is used to specify the remaining point categories to complete the clustering. Simulation experiments show that the proposed method has better clustering performance on both artificial synthetic datasets and UCI real datasets. At the same time, the feasibility and effectiveness of the method are verified by clustering the simulated air battlefield data.
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