自动目标识别评价方法发展述评

何峻 傅瑞罡 付强

何峻, 傅瑞罡, 付强. 自动目标识别评价方法发展述评[J]. 雷达学报, 2023, 12(6): 1215–1228. doi: 10.12000/JR23094
引用本文: 何峻, 傅瑞罡, 付强. 自动目标识别评价方法发展述评[J]. 雷达学报, 2023, 12(6): 1215–1228. doi: 10.12000/JR23094
HE Jun, FU Ruigang, and FU Qiang. Review of automatic target recognition evaluation method development[J]. Journal of Radars, 2023, 12(6): 1215–1228. doi: 10.12000/JR23094
Citation: HE Jun, FU Ruigang, and FU Qiang. Review of automatic target recognition evaluation method development[J]. Journal of Radars, 2023, 12(6): 1215–1228. doi: 10.12000/JR23094

自动目标识别评价方法发展述评

doi: 10.12000/JR23094
基金项目: 国家自然科学基金(62001482),湖南省自然科学基金(2021JJ40676),春雨基金(2035250204),重点实验室基金(220302)
详细信息
    作者简介:

    何 峻,博士,副教授,主要研究方向为自动目标识别、决策分析

    傅瑞罡,博士,副教授,主要研究方向为自动目标识别、深度学习

    付 强,博士,教授,主要研究方向为自动目标识别、精确制导、雷达信号处理等

    通讯作者:

    傅瑞罡 furuigang08@nudt.edu.cn

  • 责任主编:杜兰 Corresponding Editor: DU Lan
  • 中图分类号: TN957

Review of Automatic Target Recognition Evaluation Method Development

Funds: The National Natural Science Foundation of China (62001482), Hunan Provincial Natural Science Foundation of China (2021JJ40676), Spring Rain Foundation (2035250204), Key Laboratory Foundation (220302)
More Information
  • 摘要: 自动目标识别(ATR)是一个汇集模式识别、人工智能、信息处理等多学科融合发展的技术领域,ATR评价则是将ATR算法/系统等作为研究对象的评价行为。由于ATR算法/系统面临目标非合作、工作条件复杂多样、决策者自身存在多种主观偏好等诸多困难,ATR评价贯穿ATR研制的全过程,对ATR技术发展起到重要的指导作用。该文首先阐述了ATR评价方法研究的内涵,简要回顾ATR技术发展;然后从性能指标定义、测试条件构建、推断与决策等方面详细梳理分析了ATR评价方法研究的成果、应用及最新研究进展;最后总结了若干ATR评价方法研究的发展方向。该文旨在为更好地理解ATR评价和有效使用ATR评价方法提供新的参考借鉴。

     

  • 图  1  典型ATR研制与测试生命周期[71]

    Figure  1.  A typical ATR development and test life cycle[71]

    图  2  3类目标识别结果混淆矩阵[72]

    Figure  2.  Classification result map of three types of targets[72]

    图  3  双正态分布生成的ROC曲线[73]

    Figure  3.  Sample N-N ROC curve generation[73]

    图  4  实际P-R曲线与平滑后P-R曲线

    Figure  4.  Actual and smoothed P-R Curve

    图  5  MSTAR计划中的训练与测试条件[9]

    Figure  5.  Training and testing conditions in MSTAR program[9]

    图  6  10类MSTAR目标的光学及SAR图像[82]

    Figure  6.  Optic and SAR images of 10 MSTAR targets[82]

    图  7  MSTAR数据引文进展[84]

    Figure  7.  MSTAR citation progression[84]

    图  8  通用决策分析模型结构[95]

    Figure  8.  Common decision analysis model structure[95]

    表  1  常见ATR识别性能指标

    Table  1.   Common ATR performance measures

    形式 典型代表 使用要点 适用范围 优/缺点
    表格 混淆矩阵 每行数据记录一类目标被正确识别或错误混淆的情况 任意m类目标的分类性能评价 优点:记录所有目标类型之间的相互区分结果
    缺点:目标类型数m较大时展示效果不直观
    概率 检测概率PD
    虚警概率PFA
    种类识别概率PCC
    类型识别概率PID
    逐级识别过程中特定事件的发生概率 目标识别过程中某个决策任务结果的不确定性度量 优点:内涵清晰,指标点估计值计算简单
    缺点:需要根据多次目标识别试验进行统计推断
    曲线 ROC曲线
    P-R曲线
    转换为AUC, AP采用下面积、曲线积分的形式度量 相互制约的两方面
    性能综合刻画
    优点:综合评价阈值变化对两个相互制约指标的影响
    缺点:需调整阈值进行量化,精度受阈值离散取值的影响
    下载: 导出CSV
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  • 收稿日期:  2023-05-24
  • 修回日期:  2023-06-23
  • 网络出版日期:  2023-07-17
  • 刊出日期:  2023-12-28

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