基于直方图分析和自适应遗传的雷达道路目标识别特征优选方法

瓦其日体 李刚 赵志纯 则正华

瓦其日体, 李刚, 赵志纯, 等. 基于直方图分析和自适应遗传的雷达道路目标识别特征优选方法[J]. 雷达学报, 2023, 12(5): 1014–1030. doi: 10.12000/JR22245
引用本文: 瓦其日体, 李刚, 赵志纯, 等. 基于直方图分析和自适应遗传的雷达道路目标识别特征优选方法[J]. 雷达学报, 2023, 12(5): 1014–1030. doi: 10.12000/JR22245
WAQI Riti, LI Gang, ZHAO Zhichun, et al. Feature selection method of radar-based road target recognition via histogram analysis and adaptive genetics[J]. Journal of Radars, 2023, 12(5): 1014–1030. doi: 10.12000/JR22245
Citation: WAQI Riti, LI Gang, ZHAO Zhichun, et al. Feature selection method of radar-based road target recognition via histogram analysis and adaptive genetics[J]. Journal of Radars, 2023, 12(5): 1014–1030. doi: 10.12000/JR22245

基于直方图分析和自适应遗传的雷达道路目标识别特征优选方法

doi: 10.12000/JR22245
基金项目: 国家自然科学基金(62101304, 61925106),华为技术有限公司委托研发项目
详细信息
    作者简介:

    瓦其日体,硕士生,主要研究方向为毫米波雷达目标识别

    李 刚,博士,教授,博士生导师,主要研究方向为雷达信号处理、遥感、多源信息融合、数据驱动医疗健康等

    赵志纯,博士,副教授,主要研究方向为雷达信号处理、时频分析、微动特征分析、雷达目标识别技术等

    则正华,硕士生,主要研究方向为毫米波雷达信号处理

    通讯作者:

    赵志纯 zzc@smbu.edu.cn

  • 责任主编:唐世阳 Corresponding Editor: TANG Shiyang
  • 中图分类号: TN959.1

Feature Selection Method of Radar-based Road Target Recognition via Histogram Analysis and Adaptive Genetics

Funds: The National Natural Science Foundation of China (62101304, 61925106), Research and development project commissioned by Huawei Technologies Co. LTD
More Information
  • 摘要: 在雷达道路目标识别领域,目标类别多变且特性相近时增加目标特征维数是一种提高识别性能常用的手段。然而特征维数的增多会导致特征冗余和维数灾难,因此需对提取的高维特征集进行优选,基于随机搜索的自适应遗传算法(AGA)是一种有效的特征优选方法。为提升AGA算法的特征优选效率和精度,现有方法通常通过引入特征与目标种类的先验相关度对高维特征集进行预降维,然而此类算法仅考虑了单个特征与目标的相关性,忽略了特征组合与目标类别的匹配度,使得优选出的特征集不一定是目标的最佳识别组合。针对该问题,该文通过引入直方图分析对不同特征组合与目标类别的匹配度加以研究,提出了一种新的改进自适应遗传(HA-AGA)特征优选方法,在提升特征优选效率和精度的同时提升目标的识别性能。基于毫米波雷达实测数据集的对比实验表明,所提出的HA-AGA方法的目标识别平均精确率可达到95.7%,分别比IG-GA, ReliefF-IAGA和改进RetinaNet方法提升了1.9%, 2.4%和10.1%。基于公共数据集CARRADA的对比实验表明,所提出的HA-AGA方法的目标识别平均精确率达到93.0%,分别比IG-GA和ReliefF-IAGA方法提升了1.2%和1.5%,验证了所提方法的有效性和优越性。此外,还进行了不同特征优选方法分别结合集成装袋树、精细树和K-最邻近(KNN)分类器的性能对比,实验结果表明所提方法结合不同分类器均具有明显优势,具有一定的广泛适用性。

     

  • 图  1  数据预处理流程图

    Figure  1.  Data preprocessing flow chart

    图  2  基于直方图分析的特征优选流程图

    Figure  2.  Flow chart of feature optimization via histogram analysis

    图  3  数据预处理各步骤实验结果

    Figure  3.  Experimental results of each step of data preprocessing

    4  各类目标及其对应的RD谱

    4.  All kinds of targets and their corresponding RD spectrum

    图  5  各算法特征预降维结果

    Figure  5.  Pre-dimensionality reduction results of different algorithms

    图  6  各算法特征优选结果

    Figure  6.  Feature optimization results of different algorithms

    图  7  由PCA降维得到的实测数据集特征优选前后可视化分布图

    Figure  7.  Visual distribution of real data set features before and after optimization obtained by PCA dimension reduction

    图  8  不同信噪比条件下各特征选择算法识别结果

    Figure  8.  The recognition results of feature selection algorithm in different SNR

    图  9  数据预处理各步骤实验结果

    Figure  9.  Experimental results of each step of data preprocessing

    图  10  不同算法特征预降维结果

    Figure  10.  Pre-dimensionality reduction results of different algorithms

    图  11  不同算法特征优选结果

    Figure  11.  Feature optimization results of different algorithms

    图  12  由PCA降维得到的CARRADA数据集特征优选前后可视化分布图

    Figure  12.  Visual distribution of CARRADA data set features before and after optimization obtained by PCA dimension reduction

    表  1  特征及其公式

    Table  1.   Features and formulas

    序号特征公式序号特征公式
    1峰值点距离$ {f_1} = {r_{\text{t}}} $16最大距离点速度与
    峰值点速度差
    $ {f_{16}} = {f_{14}} - {f_{11}} $
    2平均距离$ {f_2} = \dfrac{1}{s}\displaystyle\sum\limits_{i = 1}^s {{r_i}} $17速度宽$ {f_{17}} = \max ({v_i}) - \min ({v_i}) $
    3最小距离点距离$ {f_3} = {\text{min}}({r_i}) $18速度维中值$ {f_{18}} = \dfrac{{\max ({v_i}) + \min ({v_i})}}{2} $
    4最大距离点距离$ {f_4} = {\text{max}}({r_i}) $19速度维方差$ {f_{19}} = \dfrac{1}{G}\displaystyle\sum\limits_{g = 1}^G {{{({A_g} - {{\bar A}_G})}^2}} $
    5峰值点距离与最小距离差$ {f_5} = {f_1} - {f_3} $20速度维熵$ {f_{20}} = - \displaystyle\sum\limits_{g = 1}^G {{p_g}\lg \left( {{p_g}} \right)} $
    6最大距离与峰值点距离差$ {f_6} = {f_4} - {f_1} $21散射点个数$ {f_{21}} = s $
    7距离宽$ {f_7} = {\text{max}}({r_i}) - {\text{min}}({r_i}) $22距离宽比速度宽$ {f_{22}} = \dfrac{{{f_7}}}{{{f_{17}}}} $
    8距离维中值$ {f_8} = \dfrac{{\max ({r_i}) + \min ({r_i})}}{2} $23能量密度$ {f_{23}} = \dfrac{1}{s}\displaystyle\sum\limits_{i = 1}^s {A_i^2} $
    9距离维方差$ {f_9} = \dfrac{1}{G}\displaystyle\sum\limits_{g = 1}^G {{{({A_g} - {{\bar A}_G})}^2}} $24波形熵$ {f_{24}} = - \displaystyle\sum\limits_{n = 0}^{N - 1} {\displaystyle\sum\limits_{m = 0}^{M - 1} {{p_{n,m}}\lg \left( {{p_{n,m}}} \right)} } $
    10距离维熵$ {f_{10}} = - \displaystyle\sum\limits_{g = 1}^G {{p_g}\lg \left( {{p_g}} \right)} $252阶中心矩$ {f_{25}} = \displaystyle\sum\limits_{n = 0}^{N - 1} {\displaystyle\sum\limits_{m = 0}^{M - 1} {(n - \bar n)(m - \bar m)A\left( {n,m} \right)} } $
    11峰值点速度$ {f_{11}} = {v_{\text{t}}} $264阶中心矩$ {f_{26}} = \displaystyle\sum\limits_{n = 0}^{N - 1} {\displaystyle\sum\limits_{m = 0}^{M - 1} {{{(n - \bar n)}^2}{{(m - \bar m)}^2}A\left( {n,m} \right)} } $
    12平均速度$ {f_{12}} = \dfrac{1}{s}\displaystyle\sum\limits_{i = 1}^s {{v_i}} $27信号幅值方差$ {f_{27}} = \dfrac{1}{s}\displaystyle\sum\limits_{i = 1}^s {{{({A_i} - \bar A)}^2}} $
    13最小距离点速度$ {f_{13}} = {v_{{r_{\min }}}} $28主分量能量$ {f_{28}} = \displaystyle\sum\limits_{j = 1}^n {{\text{main\_energ}}{{\text{y}}_j}} $
    14最大距离点速度$ {f_{14}} = {v_{{r_{\max }}}} $29副分量能量$ {f_{29}} = \displaystyle\sum\limits_{i = 1}^s {A_i^2 - } \displaystyle\sum\limits_{j = 1}^n {{\text{main\_energ}}{{\text{y}}_j}} $
    15峰值点速度与最小
    距离点速度差
    $ {f_{{\text{15}}}} = {f_{{\text{11}}}} - {f_{{\text{13}}}} $30主副能量比$ {f_{30}} = \dfrac{{{f_{28}}}}{{{f_{29}}}} $
    下载: 导出CSV

    表  2  实测数据集描述

    Table  2.   The real radar dataset description

    类别标签训练集(帧)测试集(帧)
    行人129811278
    电动车227271169
    自行车311148
    小汽车41756753
    货车51162499
    公交车61486637
    下载: 导出CSV

    表  3  雷达参数

    Table  3.   Radar parameters

    参数数值
    载频(GHz)24
    带宽(MHz)207.32
    波形斜率(MHz/μs)0.80986
    采样率(MHz)1
    每个脉冲采样点数256
    帧脉冲数128
    接收通道数4
    帧持续时长(ms)32.768
    下载: 导出CSV

    表  4  各算法优选后的特征组合

    Table  4.   Feature combination of different algorithms after optimization

    特征选择算法优选后特征组合
    IG-GA$ {f_2} $, $ {f_8} $, $ {f_{10}} $, $ {f_{11}} $, $ {f_{12}} $, $ {f_{22}} $, $ {f_{24}} $, $ {f_{26}} $, $ {f_{27}} $, $ {f_{28}} $, $ {f_{29}} $
    ReliefF-IAGA$ {f_1} $, $ {f_2} $, $ {f_3} $, $ {f_4} $, $ {f_5} $, $ {f_6} $, $ {f_8} $, $ {f_{10}} $, $ {f_{12}} $, $ {f_{17}} $, $ {f_{18}} $, $ {f_{20}} $, $ {f_{22}} $, $ {f_{25}} $
    HA-AGA$ {f_1} $, $ {f_3} $, $ {f_5} $, $ {f_7} $, $ {f_8} $, $ {f_{10}} $, $ {f_{11}} $, $ {f_{12}} $, $ {f_{14}} $, $ {f_{17}} $, $ {f_{20}} $, $ {f_{22}} $, $ {f_{23}} $, $ {f_{24}} $
    下载: 导出CSV

    表  5  各算法结合集成装袋树优选后识别结果(实测数据集)(%)

    Table  5.   Recognition result of different algorithms combined with integrated bagging tree after optimization (Real radar dataset) (%)

    类别未优选IG-GAReliefF-IAGAHA-AGA
    精确率/召回率/F测度精确率/召回率/F测度精确率/召回率/F测度精确率/召回率/F测度
    行人99.0/95.7/97.398.3/96.7/97.598.4/96.6/97.599.0/96.8/97.9
    电动车94.3/99.1/96.695.3/98.2/96.795.5/98.1/96.895.5/98.8/97.1
    自行车87.2/70.8/78.292.5/77.1/84.185.7/75.0/80.095.0/79.2/86.4
    小汽车97.6/98.3/98.096.1/97.3/96.797.8/98.9/98.398.1/98.5/98.3
    货车94.8/85.4/90.091.2/84.1/87.591.4/84.3/87.793.5/89.5/91.5
    公交车90.4/95.9/93.189.1/92.2/90.789.2/93.2/91.292.9/94.9/93.9
    平均值93.9/91.1/92.293.8/90.9/92.293.3/91.0/91.995.7/93.0/94.2
    下载: 导出CSV

    表  6  各算法结合精细树优选后识别结果(实测数据集)(%)

    Table  6.   Recognition result of different algorithms combined with fine tree after optimization (Real radar dataset) (%)

    类别未优选IG-GAReliefF-IAGAHA-AGA
    精确率/召回率/F测度精确率/召回率/F测度精确率/召回率/F测度精确率/召回率/F测度
    行人99.1/94.3/96.699.1/94.2/96.699.5/94.0/96.799.3/94.7/97.0
    电动车92.3/98.9/95.692.5/99.0/95.692.5/98.9/95.792.7/99.0/95.7
    自行车75.6/64.6/70.084.6/68.8/75.988.4/70.2/78.289.5/70.8/79.1
    小汽车96.5/97.4/96.895.9/93.5/94.797.5/97.0/97.196.4/97.5/97.0
    货车86.2/76.7/81.283.7/84.9/84.388.6/78.6/83.389.6/80.4/84.8
    公交车82.9/90.0/86.388.4/89.2/88.885.3/91.6/88.386.8/91.4/89.0
    平均值88.7/87.0/87.890.7/88.3/89.392.0/88.3/89.992.4/89.0/90.4
    下载: 导出CSV

    表  7  各算法结合KNN优选后识别结果(实测数据集)(%)

    Table  7.   Recognition result of different algorithms combined with KNN after optimization (Real radar dataset) (%)

    类别未优选IG-GAReliefF-IAGAHA-AGA
    精确率/召回率/F测度精确率/召回率/F测度精确率/召回率/F测度精确率/召回率/F测度
    行人94.6/93.6/94.196.0/94.8/95.596.0/94.3/95.196.3/94.6/95.5
    电动车91.9/94.3/93.192.7/96.1/94.392.2/95.2/93.792.7/95.7/94.2
    自行车70.7/60.4/65.287.5/72.9/79.684.7/75.0/79.586.0/77.1/81.3
    小汽车96.7/96.6/96.796.7/95.8/96.396.1/97.0/96.596.7/97.1/97.0
    货车88.6/83.3/85.985.7/82.5/84.587.2/82.8/84.989.6/83.3/86.3
    公交车87.8/91.0/89.387.9/89.4/88.690.0/90.0/90.088.4/91.6/90.0
    平均值88.4/86.4/87.491.1/88.6/89.891.0/89.0/89.991.6/89.9/91.0
    下载: 导出CSV

    表  8  HA-AGA与改进的RetinaNet方法的识别结果(%)

    Table  8.   Recognition results of HA-AGA and the improved RetinaNet method (%)

    类别改进的RetinaNetHA-AGA
    精确率/召回率/F测度精确率/召回率/F测度
    行人98.1/95.6/96.899.0/96.8/97.9
    电动车87.4/92.0/89.695.5/98.8/97.1
    自行车81.8/56.3/66.795.0/79.2/86.4
    小汽车80.2/84.5/82.398.1/98.5/98.3
    货车82.3/67.1/73.993.5/89.5/91.5
    公交车83.8/88.7/86.292.9/94.9/93.9
    平均值85.6/80.7/82.695.7/93.0/94.2
    下载: 导出CSV

    表  9  CARRADA数据集雷达参数[30]

    Table  9.   Radar parameters of CARRADA dataset[30]

    参数数值
    载频(GHz)77
    带宽(GHz)4
    最大距离(m)50
    FFT距离分辨率(m)0.20
    最大径向速度(m/s)13.43
    FFT径向速度分辨率(m/s)0.42
    每个脉冲采样点数256
    帧脉冲数64
    下载: 导出CSV

    表  10  CARRADA数据集描述

    Table  10.   CARRADA dataset description

    类别标签训练集(帧)测试集(帧)
    行人123831021
    自行车21309561
    小汽车323931026
    下载: 导出CSV

    表  11  不同算法优选后的特征组合

    Table  11.   Feature combination of different algorithms after optimization

    特征选择算法优选后特征组合
    IG-GA$ {f_2} $,$ {f_4} $,$ {f_7} $,$ {f_8} $,$ {f_{12}} $,$ {f_{13}} $,$ {f_{18}} $,$ {f_{20}} $,$ {f_{22}} $,$ {f_{23}} $,$ {f_{26}} $
    ReliefF-IAGA$ {f_1} $,$ {f_4} $,$ {f_5} $,$ {f_7} $,$ {f_8} $,$ {f_{12}} $,$ {f_{13}} $,$ {f_{18}} $,$ {f_{23}} $,$ {f_{26}} $,$ {f_{27}} $,$ {f_{30}} $
    HA-AGA$ {f_1} $,$ {f_2} $,$ {f_3} $,$ {f_4} $,$ {f_6} $,$ {f_7} $,$ {f_8} $,$ {f_{12}} $,$ {f_{13}} $,$ {f_{17}} $,$ {f_{18}} $,$ {f_{21}} $,$ {f_{23}} $,$ {f_{28}} $
    下载: 导出CSV

    表  12  各算法结合集成装袋树优选后识别结果(CARRADA)(%)

    Table  12.   Recognition result of different algorithms combined with integrated bagging tree after optimization (CARRADA) (%)

    类别未优选IG-GAReliefF-IAGAHA-AGA
    精确率/召回率/F测度精确率/召回率/F测度精确率/召回率/F测度精确率/召回率/F测度
    行人90.3/93.3/91.890.5/94.4/92.489.8/94.4/92.191.7/95.2/93.4
    自行车85.1/83.4/84.387.2/83.6/85.487.2/82.4/84.789.5/85.4/87.4
    小汽车97.6/95.4/96.597.6/95.6/96.697.4/95.4/96.497.7/96.5/97.1
    平均值91.0/90.7/90.991.8/91.2/91.591.5/90.7/91.193.0/92.4/92.7
    下载: 导出CSV

    表  13  各算法结合精细树优选后识别结果(CARRADA)(%)

    Table  13.   Recognition result of different algorithms combined with fine tree after optimization (CARRADA) (%)

    类别未优选IG-GAReliefF-IAGAHA-AGA
    精确率/召回率/F测度精确率/召回率/F测度精确率/召回率/F测度精确率/召回率/F测度
    行人87.5/86.7/87.186.9/86.6/86.884.0/88.0/86.088.0/87.3/87.7
    自行车74.8/80.6/77.671.4/80.4/75.673.5/76.3/74.972.8/83.1/77.6
    小汽车95.5/92.4/93.996.5/90.2/93.296.2/89.8/92.997.6/90.9/94.2
    平均值85.9/86.6/86.284.9/85.7/85.284.5/84.7/84.686.0/87.1/86.5
    下载: 导出CSV

    表  14  各算法结合KNN优选后识别结果(CARRADA)(%)

    Table  14.   Recognition result of different algorithms combined with KNN after optimization (CARRADA) (%)


    类别
    未优选IG-GAReliefF-IAGAHA-AGA
    精确率/召回率/F测度精确率/召回率/F测度精确率/召回率/F测度精确率/召回率/F测度
    行人84.9/86.2/85.588.1/87.5/87.886.5/84.4/85.588.7/88.7/88.7
    自行车76.4/75.4/75.976.7/75.6/76.274.1/75.9/75.076.9/79.1/78.0
    小汽车93.0/92.2/92.693.4/94.7/94.192.4/93.4/92.993.1/91.6/92.3
    平均值84.8/84.6/84.786.1/85.9/86.084.3/84.6/84.586.2/86.5/86.4
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-12-31
  • 修回日期:  2023-02-22
  • 网络出版日期:  2023-03-13
  • 刊出日期:  2023-10-28

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