基于三特征预测的海杂波中小目标检测方法

董云龙 张兆祥 丁昊 黄勇 刘宁波

董云龙, 张兆祥, 丁昊, 等. 基于三特征预测的海杂波中小目标检测方法[J]. 雷达学报, 2023, 12(4): 762–775. doi: 10.12000/JR23037
引用本文: 董云龙, 张兆祥, 丁昊, 等. 基于三特征预测的海杂波中小目标检测方法[J]. 雷达学报, 2023, 12(4): 762–775. doi: 10.12000/JR23037
DONG Yunlong, ZHANG Zhaoxiang, DING Hao, et al. Target detection in sea clutter using a three-feature prediction-based method[J]. Journal of Radars, 2023, 12(4): 762–775. doi: 10.12000/JR23037
Citation: DONG Yunlong, ZHANG Zhaoxiang, DING Hao, et al. Target detection in sea clutter using a three-feature prediction-based method[J]. Journal of Radars, 2023, 12(4): 762–775. doi: 10.12000/JR23037

基于三特征预测的海杂波中小目标检测方法

doi: 10.12000/JR23037
基金项目: 国家自然科学基金(62101583, 61871392),泰山学者工程(tsqn202211246)
详细信息
    作者简介:

    董云龙,博士,教授,主要研究方向为多传感器信息融合、雷达目标检测与跟踪

    张兆祥,硕士生,主要研究方向为海杂波中目标检测

    丁 昊,博士,副教授,主要研究方向为海杂波特性认知与抑制、海杂波中目标检测

    黄 勇,博士,副教授,主要研究方向为雷达目标检测、MIMO雷达信号处理

    刘宁波,博士,副教授,主要研究方向为雷达信号智能处理、海上目标探测技术

    通讯作者:

    丁昊 hao3431@tom.com

    黄勇 huangyong2003@163.com

  • 责任主编:许述文 Corresponding Editor: XU Shuwen
  • 中图分类号: TN957.51

Target Detection in Sea Clutter Using a Three-feature Prediction-based Method

Funds: The National Natural Science Foundation of China (62101583, 61871392), The Taishan Scholars Program (tsqn202211246)
More Information
  • 摘要: 特征检测方法是解决海杂波中小目标检测问题的重要途径,其根据特征值是否在判决区域内判断目标有无,几乎不考虑特征间的时序信息。事实上,历史帧数据与当前帧数据的时序关联性,可以为当前帧特征值的计算提供丰富的先验信息。为此,该文提出了一种使用自回归(AR)模型在特征域对雷达回波进行时序建模和预测的方法,以利用历史帧特征的先验信息。首先,使用AR模型对平均幅度(AA)、相对多普勒峰高(RDPH)、频谱峰均比(FPAR)特征序列进行建模和1步预测分析,验证了对特征序列进行AR建模和预测的可行性。其次,提出利用历史帧特征时序信息作为先验信息的特征值提取方法,在此基础上,提出一种基于三特征预测的小目标检测方法,该方法可有效利用AA, RDPH和FPAR的历史帧特征时序信息。最后,使用实测数据验证了所提方法的有效性。

     

  • 图  1  帧间特征值的时序关系示意图

    Figure  1.  Schematic diagram of the temporal relationship of the features between frames

    图  2  3种特征的平均自相关系数与平均偏自相关系数

    Figure  2.  Average autocorrelation coefficient and average partial autocorrelation coefficient for the three features

    图  3  BIC估计的3种特征序列的AR模型最佳阶数

    Figure  3.  Optimal order of AR models for the three feature sequences estimated by the BIC

    图  4  3种特征的AR模型拟合结果(海杂波)

    Figure  4.  AR model fitting results for three features (sea clutter)

    图  5  3种特征的AR模型1步预测结果

    Figure  5.  1-step prediction results of AR models with three features

    图  6  3种特征的AR模型拟合结果(含目标回波)

    Figure  6.  AR model fitting results for three features (target echo)

    图  7  利用先验信息的特征提取方法示意图

    Figure  7.  The schematic diagram of the feature extraction method using prior information

    图  8  基于三特征预测的检测器工作流程图

    Figure  8.  The workflow diagram of the detector based on three-feature prediction

    图  9  两种特征空间内的巴氏距离对比

    Figure  9.  Comparison of B-distance in two feature spaces

    图  10  加权系数对检测概率的影响

    Figure  10.  The effect of weighting coefficient on detection probabilities

    图  11  4种检测器的检测概率

    Figure  11.  Detection probabilities of the four detectors

    表  1  3种特征的预测结果

    Table  1.   Predicted results of the three features

    数据平均幅度相对多普勒峰高频谱峰均比
    误差均值误差标准差误差(%)误差均值误差标准差误差(%)误差均值误差标准差误差(%)
    #170.00020.21677.58–0.00490.171611.46–0.07462.639415.51
    #26–0.00740.179010.90–0.00670.198913.11–0.22544.213116.72
    #30–0.00870.157610.05–0.00690.202913.45–0.23534.088917.54
    #31–0.01140.176911.42–0.00690.200213.21–0.24624.132517.43
    #40–0.01350.210112.10–0.00720.203613.36–0.28314.413118.25
    #54–0.00480.199111.53–0.00550.182112.59–0.15363.112715.31
    #280–0.01140.175110.87–0.00590.186412.21–0.23164.269917.04
    #310–0.00640.12807.71–0.00460.172211.26–0.10232.370713.11
    #311–0.00670.13308.57–0.00510.185012.09–0.12162.709514.12
    #320–0.00550.12818.04–0.00650.203513.75–0.12292.673314.34
    下载: 导出CSV

    表  2  1993年IPIX雷达数据说明

    Table  2.   The description of IPIX radar data collected in 1993

    序号数据名称浪高(m)风速(km/h)目标所在单元受影响单元
    1#172.2998, 10, 11
    2#261.1976, 8
    3#300.91976, 8
    4#310.91976, 8, 9
    5#401.0975, 6, 8
    6#540.72087, 9, 10
    7#2801.61087, 10
    8#3100.93376, 8, 9
    9#3110.93376, 8, 9
    10#3200.92876, 8, 9
    下载: 导出CSV

    表  3  重叠脉冲数对本文所提检测器的影响

    Table  3.   The effect of the number of overlapping pulses on the detector proposed in this paper

    重叠脉冲数#30#31#310
    HHVVHVHHVVHVHHVVHV
    00.2700.3810.4170.3160.5190.5410.5730.2550.604
    640.2810.3710.3690.3240.5350.5740.5960.2890.597
    1280.3450.4570.4850.4120.5780.6370.6020.3100.649
    下载: 导出CSV

    表  4  历史帧窗口长度对本文所提检测器的影响

    Table  4.   The effect of historical frame window length on the detector proposed in this paper

    历史帧窗口长度#30#31#310
    HHVVHVHHVVHVHHVVHV
    250.3040.4330.4350.3960.5390.6280.6000.3050.641
    500.3170.4460.4620.4000.5610.6350.6020.3120.647
    1000.3450.4570.4850.4120.5780.6370.6020.3100.649
    下载: 导出CSV

    表  5  脉冲数对4种检测器的影响

    Table  5.   The effect of the number of pulses on the four detectors

    脉冲数检测器#17#26#320
    HHVVHVHHVVHVHHVVHV
    128一致性因子检测器[26]0.2720.0350.2140.1510.2340.2000.3630.1080.469
    文献[22]检测器0.5210.2370.5440.2860.3660.4730.5530.5560.825
    原三特征检测器0.5950.2570.5090.2110.3890.4660.6280.4870.754
    所提检测器0.6420.3040.5440.2810.4030.5280.7450.6740.855
    256一致性因子检测器[26]0.3680.0620.2200.1850.2810.2490.3190.1150.461
    文献[22]检测器0.5950.2020.5100.3610.5550.5780.6540.6930.840
    原三特征检测器0.6270.3100.5370.3430.5650.6450.6880.5940.763
    所提检测器0.7160.3890.6660.4740.5670.7010.7870.7480.873
    512一致性因子检测器[26]0.3450.0580.2150.2000.3140.2630.2210.1210.470
    文献[22]检测器0.6210.2010.5210.4410.5810.5900.7580.7640.855
    原三特征检测器0.6330.3530.6200.4250.6130.6860.6570.6250.845
    所提检测器0.7310.4280.7920.5440.6200.7470.8030.7830.908
    下载: 导出CSV
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  • 收稿日期:  2023-03-23
  • 修回日期:  2023-05-11
  • 网络出版日期:  2023-05-31
  • 刊出日期:  2023-08-28

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