樊玉琦,刘瑜岚,许 雄,郭 丹,温鹏飞.基于点迹时空关系的雷达目标航迹识别[J].电子测量与仪器学报,2020,34(9):108-116 |
基于点迹时空关系的雷达目标航迹识别 |
Radar target track recognition based on spatial-temporal relationship of track points |
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DOI: |
中文关键词: 雷达 目标航迹识别 属性选择 间距值特征 递归神经网络 |
英文关键词:radar target track recognition attribute selection interval value feature recurrent neural network (RNN) |
基金项目:国家自然科学基金(61701162)、电子信息系统复杂电磁环境效应国家重点实验室开放课题(CEMEE2018Z0102B)资助项目 |
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中文摘要: |
雷达目标航迹的识别有助于指挥员判断对方作战意图和作战任务,从而为战场决策提供协助作用,但目前少有文献研
究目标航迹识别问题。 根据雷达检测到的目标物体的时间、距离和方位等时空数据,研究目标航迹识别问题,并提出了一种基
于雷达点迹数据时空关系的目标航迹识别算法(TRST)。 该算法首先对雷达点迹数据进行属性选择,然后挖掘点迹数据在空间
关系上的间距值特征,最后构建递归神经网络进一步捕捉点迹数据的时空关系特征,实现对目标航迹的分类识别。 实验结果表
明,TRST 算法能够有效提高目标航迹识的准确率、精确率、召回率和 F1-Score 性能。 |
英文摘要: |
Radar target track recognition can help a commander to estimate the intention and tasks of the adversary, and facilitate the
decision making of the commander. However, little attention has been paid on the radar target track recognition problem. Given the radar
target data such as time, distance, direction, and so on, a radar target track recognition based on spatial-temporal relationship of track
points is proposed to solve the problem of target track recognition. The algorithm selects the attributes of radar track data and then
extracts the space characteristics of the track points in the spatial domain. The algorithm constructs the recursive neural network to
capture the characteristics of the track points in the time domain and realize the classification and recognition of the target track. We
conduct experiments through simulations, and the simulation results show that the proposed algorithm TRST can effectively improve the
target track recognition performance in terms of accuracy, precision, recall and F1-Score. |
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