基于参数化统计模型的雷达HRRP目标识别方法综述

陈健 杜兰 廖磊瑶

陈健, 杜兰, 廖磊瑶. 基于参数化统计模型的雷达HRRP目标识别方法综述[J]. 雷达学报, 待出版. doi: 10.12000/JR22127
引用本文: 陈健, 杜兰, 廖磊瑶. 基于参数化统计模型的雷达HRRP目标识别方法综述[J]. 雷达学报, 待出版. doi: 10.12000/JR22127
CHEN Jian, DU Lan, and LIAO Leiyao. Survey of radar HRRP target recognition based on parametric statistical model[J]. Journal of Radars, in press. doi: 10.12000/JR22127
Citation: CHEN Jian, DU Lan, and LIAO Leiyao. Survey of radar HRRP target recognition based on parametric statistical model[J]. Journal of Radars, in press. doi: 10.12000/JR22127

基于参数化统计模型的雷达HRRP目标识别方法综述

doi: 10.12000/JR22127
基金项目: 国家自然科学基金(U21B2039),雷达信号处理国家级重点实验室支持计划(JKW202105),中央高校基本科研业务费专项资金(XJS210219)
详细信息
    作者简介:

    陈 健(1990-),男,山西寿阳人,博士,西安电子科技大学华山准聘副教授,硕士生导师。于2020年8月在西安电子科技大学获得博士学位,同年11月入职西安电子科技大学雷达信号处理国家重点实验室。主要研究方向为雷达目标检测识别与机器学习。在IEEE TCYB/TGRS/TAES, PR, INS, SP, ESWA等信号处理和模式识别领域知名期刊发表论文15篇,国际雷达会议文章2篇,授权专利2项,担任Signal Processing期刊审稿人,研究成果受邀在2020 IEEE Radar Conference、2021 CIE International Conference on Radar、第1届雷达学报博士论坛、第1届雷达未来之星论坛等国内外知名学术会议上报告交流。曾获第1届雷达学报博士论坛最佳学术报告奖

    杜 兰(1980-),女,河北深泽人,博士,教授。2007年在西安电子科技大学电子工程学院获得博士学位,2007年8月至2009年9月在美国杜克大学电子与计算机工程系做博士后访问研究,现为西安电子科技大学电子工程学院教授。主要研究方向为雷达目标识别、雷达信号处理、机器学习。在IEEE Trans. SP, JMLR, ESWA, IS, PR, IEEE Trans. GRS, IEEE Trans. AES, IEEE J-STARS和NIPS等国内外期刊、知名国际会议以第一作者或通信作者发表论文80余篇,授权国家/国防专利20余项,多次在国际、国内会议做特邀报告,并多次获优秀会议论文奖

    廖磊瑶(1996-),女,江西抚州人,西安电子科技大学雷达信号处理国家重点实验室博士研究生,研究方向为雷达目标识别、机器学习等

    通讯作者:

    杜兰 dulan@mail.xidian.edu.cn

  • 中图分类号: TN957.5

Survey of Radar HRRP Target Recognition Based on Parametric Statistical Model

Funds: The National Natural Science Foundation of China (U21B2039), The Stabilization Support of National Radar Signal Processing Laboratory (JKW202105), Fundamental Research Funds for the Central Universities (XJS210219)
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  • 摘要: 现代战争日趋信息化和智能化,雷达自动目标识别技术(RATR)在国家安全防卫和战略预警等军事应用方面发挥着更加重要的作用。高分辨距离像(HRRP)反映了目标散射点沿雷达视线方向的分布情况,包含了目标丰富的结构信息,对目标识别十分有价值,已成为RATR领域的研究热点。参数化统计建模旨在构建参数化数学模型表征观测数据的分布特性,是估计数据概率分布和挖掘数据隐含信息的重要手段。基于参数化统计模型的雷达HRRP目标识别就是在对HRRP参数化统计建模的基础上,直接利用估计的概率分布进行统计识别或将获取的隐含信息输入分类器进行识别。由于模型具有可融入一定的先验知识、扩展灵活、提供待求参数的不确定性评价以及能结合贝叶斯理论实现自动定阶等优势,基于参数化统计模型的HRRP识别方法整体识别性能优于其他方法,是目前HRRP识别的重点研究方向。该文从浅层和深层参数化统计建模两方面,对近15年的雷达HRRP目标识别方法进行了归纳总结,并分析了各类方法的特点和存在的问题,最后对基于HRRP参数化统计建模的雷达目标识别发展方向进行了展望。

     

  • 图  1  HRRP示意图

    Figure  1.  Sketch map of HRRP

    图  2  雷达HRRP目标识别方法分类及对比

    Figure  2.  Classification and comparison of radar HRRP target recognition methods

    图  3  典型HRRP距离单元回波幂次变换前后的统计直方图和高斯概率密度函数拟合曲线

    Figure  3.  Statistical histograms and fitting curves with Gaussian probability density function for typical HRRP range cell echoes before and after power transformation

    图  4  概率散射中心模型[32]所提HRRP散射点示例和识别结果对比

    Figure  4.  Example of HRRP scatterers extracted by the probabilistic scattering center model[32] and comparison of recognition results via different methods

    图  5  基于DP先验的变分自编码器[34]对多模分布数据的聚类过程示意图

    Figure  5.  Sketch map of clustering on data with multimode distribution via the variational autoencoder[34] based on DP prior

    图  6  参数化HRRP统计建模方法分类

    Figure  6.  Classification of parametric statistical modeling methods for HRRP

    图  7  浅层HRRP参数化统计模型分类及特点

    Figure  7.  Classification and characteristic of shallow parametric statistical model for HRRP

    图  8  实测飞机目标HRRP样本某一距离单元的分布特性示例

    Figure  8.  The distribution of a certain dimensional range cell echoes of HRRPs from an airplane target

    图  9  基于伽马-高斯混合独立双分布模型的HRRP距离单元回波分布拟合示例

    Figure  9.  The fitted distributions of measured range cell echos from an airplane target via the Gamma-Gaussian mixture two-distribution compounded statistical model

    图  10  实测飞机目标不同方位帧HRRP样本的归一化协方差矩阵示例

    Figure  10.  Normalized covariances of HRRPs from different frames of airplane target

    图  11  实测飞机目标HRRP频幅分量统计柱状图及混合高斯概率密度函数拟合示例

    Figure  11.  The histograms and mixture-Gaussian fitting curves of HRRP frequency amplitude components from measured airplane targets

    图  12  不同模型在3类实测飞机目标HRRP数据上的识别性能对比

    Figure  12.  Recognition performance comparison of different methods on measured HRRP data from three airplane targets

    图  13  不同模型在3类实测飞机目标HRRP数据上的识别率混淆矩阵

    Figure  13.  Confusion matrices of recognition rates via different models for HRRPs from three airplane targets

    图  14  FA模型在3类飞机目标HRRP数据上的识别率随训练样本数的变化

    Figure  14.  Variation of recognition rates with the number of training HRRP samples via the FA model

    图  15  FA模型与CFA模型在每帧10个训练HRRP样本情况下学出的字典原子示例

    Figure  15.  Examples of the learned dictionary atoms via the FA model and CFA model under the condition of 10 HRRP samples per frame

    图  16  CFA模型与LC-CFA模型在每帧10个训练HRRP样本情况下的隐变量系数矩阵图

    Figure  16.  Graph representations of similarity matrices of the learned weight vectors via the CFA model and LC-CFA model, under the condition of 10 HRRPs per frame

    图  17  不同统计模型在3类实测飞机目标HRRP数据上的识别率随训练样本数的变化曲线

    Figure  17.  Variation of recognition performance with the training sample number via the different statistical models on measured HRRP data from three airplane targets

    图  18  DMR-MFA模型在两类人工数据集上的聚类结果及隐变量学习结果

    Figure  18.  Results of clustering and latent variable learning via the DMR-MFA model

    图  19  基于谱图特征的单任务HMM与多任务HMM识别率随训练样本数的变化[41]

    Figure  19.  Variation of recognition rates of single-task and multi-task HMMs with the training data size on HRRP spectrogram features[41]

    图  20  不同模型在3类飞机目标HRRP数据上的识别结果随信噪比的变化[32]

    Figure  20.  Variation of recognition rates with Signal to Noise Ratio (SNR) via different models on HRRPs from three airplane targets[32]

    图  21  深层HRRP参数化统计模型分类及特点

    Figure  21.  Classification and characteristics of deep HRRP parametric statistical models

    图  22  VAE结构示意图

    Figure  22.  The structure of VAE

    图  23  不同模型对3类飞机目标HRRP数据所提特征的二维可视化

    Figure  23.  Two-dimensional visualization of features from HRRP data of three airplanes via different models

    图  24  FDCVAE网络结构图[45]

    Figure  24.  The structure of FDCVAE[45]

    图  25  FDCVAE所提3类飞机目标HRRP数据特征的可视化[45]

    Figure  25.  Visualization of HRRP features from three airplanes via the FDCVAE[45]

    图  26  CFCVAE网络结构图[63]

    Figure  26.  The structure of CFCVAE[63]

    图  27  DMVAE网络结构图[34]

    Figure  27.  Architecture of the DMVAE[34]

    图  28  DMVAE对3类飞机目标HRRP数据的聚类结果可视化[34]

    Figure  28.  Clustering visualization of HRRPs from three airplanes via the DMVAE[34]

    图  29  单一解码器VAE和DMVAE模型所提HRRP样本隐特征的二维可视化

    Figure  29.  Two-dimensional visualizations of HRRP features extracted by the VAE with single decoder and DMVAE, respetively

    图  30  不同模型在HRRP数据集上的识别性能的比较[34]

    Figure  30.  Comparison of recognition performance of different methods on HRRP data[34]

    图  31  非统计RNN结构示意图[46]

    Figure  31.  The structure of non-statistical RNN HRRP segments[46]

    图  32  HRRP样本不同数据段组成输入序列

    Figure  32.  The input sequence composed by different

    图  33  不同模型在不同聚类个数情况下的识别性能变化曲线[46]

    Figure  33.  Recognition performance curves of different models under different clustering number[46]

    图  34  DPmTRNN对各HRRP测试样本数据段的聚类结果可视化[46]

    Figure  34.  Visualization of clustering results of DPmTRNN on each HRRP sample[46]

    图  35  s-rGBN的基本结构[47]

    Figure  35.  Basic structure of s-rGBN[47]

    表  1  不同独立统计建模方法比较

    Table  1.   Comparison of different independent statistical modeling methods

    方法优点缺点
    单模分布模型计算简单、易实现。各距离单元回波分布复杂性考虑不足,对多模分布距离单元回波的拟合精度低。
    多模分布模型能够准确拟合具有多模特性的距离单元回波分布。计算复杂,模态个数需要人为预设,易造成过拟合/欠拟合。
    半参数化模型避免了多模分布模型中的模态个数预设问题,
    实现模态数随数据的自适应确定。
    引入非参数化统计方法对训练样本数的需求大大增加。
    下载: 导出CSV

    表  2  不同自回归模型比较

    Table  2.   Comparison of different autoregressive models

    方法优点缺点
    单高斯自回归模型计算简单、易实现;考虑了HRRP距离单元回波之间的相关性,能够反映HRRP的频谱幅度变化特性。难以对呈多模分布的频幅分量进行高精度拟合,整体描述能力有限。
    混合自回归模型对各频幅分量采用多个单高斯自回归模型描述,可以表征频幅分量的多模分布特性,建模精度更高。需要预先设定混合成分个数,对人工经验和先验认知依赖性强,数据自适应性能力差。
    下载: 导出CSV

    表  3  不同子空间学习模型对比

    Table  3.   Comparison of different subspace learning models

    方法代表性模型优点缺点
    基于PCA的子空间学习模型子空间近
    似模型
    保留信号主要成分、实现数据降维,一定程度抑制信号中的噪声成分。忽略了噪声子空间中包含的支撑区长度信息,数据信息利用不全。
    PPCA
    模型
    保留了噪声子空间的部分统计信息,数据信息利用更充分。要求信号子空间的基正交、支撑区内外噪声方差相同,灵活性不强。


    FA







    统计
    识别
    传统FA
    模型
    不要求投影各列正交、支撑区内外噪声方差不要求相同,建模更灵活、准确。模型复杂度高、训练样本需求量大,小样本识别能力弱。
    MTL-FA
    CFA
    LA-FA
    LC-CFA
    引入多任务学习/卷积操作/标签约束策略降低模型复杂度、提升模型类间可分性,小样本识别能力明显提升。假设HRRP服从单高斯分布,对HRRP的多模分布特性描述不足。
    LFA
    DMR-MFA
    多个描述不同分布的子FA模型集成拟合HRRP的多模分布特性。子模型个数需要提前预设,预设合理性难以保证,影响识别精度。
    特征
    提取
    MMFA
    MMRFA
    特征提取与分类器学习联合进行,增强特征可分性的同时避免了特征与分类器的失配。单高斯FA建模,对HRRP数据的多模分布特性描述能力不足。
    下载: 导出CSV

    表  4  不同时序模型对比

    Table  4.   Comparison of different temporal models

    方法优点缺点
    基于HRRP样本间时序信息挖掘的模型能够利用HRRP样本间的时序相关性进行目标识别。数据降维预处理丢失了一部分可用信息;要求训练和测试阶段目标相对雷达视线的转角保持一致,实际很难满足;无法对单个HRRP样本给出实时判决结果。
    基于HRRP样本内距离单元回波间时序信息挖掘的模型对各HRRP样本内的不同距离单元回波建立时序模型,挖掘样本距离单元之间的时序信息。无需降维、不要求训练和测试阶段目标相对雷达视线转动的一致性、能够给出各HRRP样本的实时判决结果。对各距离单元回波进行单高斯分布建模,未充分考虑距离单元回波存在的非高斯特性,建模精度仍有提升空间。
    下载: 导出CSV

    表  5  TSB-HMM模型对3类飞机目标HRRP数据的识别率混淆矩阵(%)[62]

    Table  5.   Confusion matrix of recognition rate via the TSB-HMM on three types of airplanes (%)[62]

    识别目标安-26奖状雅克-42
    安-2690.89.83.5
    奖状8.783.02.0
    雅克-420.57.294.5
    平均识别率 89.4
    下载: 导出CSV

    表  6  不同变分自编码模型比较

    Table  6.   Comparison of different VAE models

    方法优点缺点
    单解码器VAE综合统计模型的数据分布描述能力、深度网络的数据分层学习能力以及HRRP回波特性,能够提取较浅层模型表征力更强的内隐特征。对大规模复杂分布HRRP数据的拟合精度仍有限、特征可分性仍有提升空间。
    多解码器集成VAE综合多个对部分观测数据具有高精度描述能力的子解码网络实现对全部HRRP数据分布的准确表达,具备挖掘类间细微差异信息的能力,有利于难分样本类别的准确判断。训练计算复杂度高、模型学习效率低、存储需求量大。
    下载: 导出CSV

    表  7  不同模型在3类飞机目标HRRP数据上的识别率比较

    Table  7.   Comparison of recognition rates of different models on HRRP data from three airplanes

    模型识别率(%)
    AGC[37]87.08
    PCA[39]89.92
    FA[39]91.48
    分层贝叶斯信号模型[32]90.01
    原始HRRP+LSVM[64]85.08
    VAE+线性SVM[64]86.31
    RVAE+线性SVM[30]92.12
    FDCVAE[45]93.25
    CFCVAE[63]95.49
    下载: 导出CSV

    表  8  不同深层时序模型比较

    Table  8.   Comparison of different deep temporal models

    方法优点缺点
    贝叶斯RNN结合贝叶斯理论可以实现对HRRP不同局部信号非平稳时序关系的准确建模,数据时序特性挖掘更准确。仅考虑数据时间维信息的深度挖掘,结构信息的深层次提取能力有限,特征表达能力不全面。
    循环伽马置信网络同时对HRRP时间维时序信息和空间维结构信息进行深度挖掘,特征对数据的表征性更强。建模复杂、推理困难,需要选择合适的参数先验分布保证参数求解的可行性、灵活性低。
    下载: 导出CSV

    表  9  不同时序模型在实测HRRP数据上的识别准确率比较[46]

    Table  9.   Comparison of recognition accuracies via different temporal models on measured HRRP data[46]

    模型识别率(%)
    HMM87.24
    RNN88.31
    GmTRNN92.54
    下载: 导出CSV
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