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面向目标机动类型的数据标签生成方法

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针对现有目标机动类型识别方法通用性不足、准确率较低等问题,提出基于改进K-medoids聚类的多层标签生成方法.以空中目标机动轨迹为例,设计多种数据预处理方法,采用动态时间规整作为聚类算法的距离度量,通过构建标签框架指导算法实施过程,进行迭代聚类以生成多层标签.在公开数据集上测试算法,实验结果表明,该方法在无监督情况下对第一层标签的识别准确率达到 89.75%,接近传统有监督算法;同时,相对于没有引入标签框架的情况,能更有效地区分第二层模糊标签.该方法只需少量专家知识,便能简单地扩展到不同领域、不同机动类型.
Target maneuver type oriented data label generation method
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 insuffi-cient 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 algo-rithm,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 condi-tions,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.

label generationdynamic time warpingK-medoids clustering

汪其林、曹志敏、高静

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江苏自动化研究所,江苏 连云港 222061

标签生成 动态时间规整 K-medoids聚类

2024

指挥控制与仿真
中国船舶重工集团公司 第七一六研究所

指挥控制与仿真

CSTPCD
影响因子:0.309
ISSN:1673-3819
年,卷(期):2024.46(3)
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