Target maneuver type oriented data label generation method

Abstract

Target maneuver type recognition is a key aspect in the fields such as object tracking and intention recognition. A multi-layer label generation method based on improved K-medoids clustering is proposed to address the problems of insufficient universality and low accuracy of existing maneuver type recognition methods. Taking the maneuver trajectory of aerial targets as an example, various data preprocessing methods are designed, and dynamic time warping is used as the distance metric for clustering algorithm. By constructing a label framework to guide the implementation process of the clustering algorithm, iterative clustering is carried out to generate multi-layer labels. The algorithm is tested on a public dataset, and the experimental results showed that the recognition accuracy of the first layer label achieved 89.75% under unsupervised conditions, which is closed to traditional supervised algorithms; Meanwhile, compared to the condition without introducing a label framework, it is more effective in distinguishing the second layer of fuzzy labels.This method requires only a small amount of expert knowledge and can be easily extended to different fields and different maneuver type.

Keywords

• label generation|dynamic time warping|k-medoids clustering