Volume 11 Issue 6
Dec.  2022
Turn off MathJax
Article Contents
Article Navigation >  Journal of Radars  > 2022  >  11(6): 1020-1047
CHEN Jian, DU Lan, and LIAO Leiyao. Survey of radar HRRP target recognition based on parametric statistical model[J]. Journal of Radars, 2022, 11(6): 1020–1047. 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, 2022, 11(6): 1020–1047. doi: 10.12000/JR22127
shu

Survey of Radar HRRP Target Recognition Based on Parametric Statistical Model

DOI: 10.12000/JR22127

  • National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China

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)
More Information
  • Corresponding author: DU Lan, dulan@mail.xidian.edu.cn
  • Received Date: 2022-06-29
  • Rev Recd Date: 2022-07-29
    • Available Online: 2022-08-02
  • Publish Date: 2022-08-22
    Abstract
  • In the gradually becoming information-based and intelligent modern warfare, Radar Automatic Target Recognition (RATR) technology plays an increasingly important role in military applications, such as national security defense and strategic early warning. The High-Resolution Range Profile (HRRP) reflects the distribution of target scatterers along the radar line of sight and contains a target’s rich structural information, thus being valuable for target recognition and having become a research hotspot in the field of RATR. Parametric statistical modeling aims to construct a parametric mathematical model to characterize the distribution of observed data. It is an important way to estimate the data probability distribution and mine the hidden information of data. Radar HRRP target recognition based on a parametric statistical model directly uses the estimated probability distribution for statistical recognition or inputs the extracted information hidden in data into the classifier for target recognition. The parametric statistical model exhibits advantages in prior knowledge integration, flexible expansion, parameter uncertainty evaluation, and automatic order determination combined with Bayesian theory; therefore, the overall performance of the HRRP recognition method based on such a model is better than that of other methods. Therefore, parametric statistical modeling is currently the key research direction for radar HRRP recognition. This paper summarizes the radar HRRP target recognition methods of the last 15 years from the two aspects of shallow statistical modeling and deep statistical modeling, analyzes the characteristics and problems of these methods, and forecasts the development direction of radar target recognition based on HRRP parametric statistical modeling.

     

    • FullText(HTML)
  • loading
    • References(66)
  • [1]
    SKOLNIK M I. Introduction to Radar Systems[M]. 2nd ed. New York: McGraw-Hill, 1980.
    [2]
    向敬成, 张明友. 雷达系统[M]. 北京: 电子工业出版社, 2001.

    XIANG Jingcheng and ZHANG Mingyou. Radar System[M]. Beijing: Publishing House of Electronics Industry, 2001.
    [3]
    丁鹭飞, 耿富录. 雷达原理[M]. 3版. 西安: 西安电子科技大学出版社, 2002.

    DING Lufei and GENG Fulu. Principle of Radar[M]. 3rd ed. Xi’an: Xidian University Press, 2002.
    [4]
    SKOLNIK M I, 王军, 林强, 米慈中, 等译. 雷达手册[M]. 2版. 北京: 电子工业出版社, 2003.

    SKOLNIK M I, WANG Jun, LIN Qiang, MI Cizhong, et al. translation. Radar Handbook[M]. 2nd ed. Beijing: Publishing House of Electronics Industry, 2003.
    [5]
    刘宏伟, 杜兰, 袁莉, 等. 雷达高分辨距离像目标识别研究进展[J]. 电子与信息学报, 2005, 27(8): 1328–1334.

    LIU Hongwei, DU Lan, YUAN Li, et al. Progress in radar automatic target recognition based on high range resolution profile[J]. Journal of Electronics &Information Technology, 2005, 27(8): 1328–1334.
    [6]
    杜兰. 飞机目标的雷达回波特性研究[D]. [硕士论文], 西安电子科技大学, 2004.

    DU Lan. Research on the characteristics of the radar echoes from aircraft targets[D]. [Master dissertation], Xidian University, 2004.
    [7]
    单凯晶, 肖怀铁, 朱俊. 基于C-均值聚类的高分辨距离像识别[J]. 现代雷达, 2010, 32(6): 49–53. doi: 10.16592/j.cnki.1004-7859.2010.06.025

    SHAN Kaijing, XIAO Huaitie, and ZHU Jun. High resolution range profiles recognition based on C-means clustering[J]. Modern Radar, 2010, 32(6): 49–53. doi: 10.16592/j.cnki.1004-7859.2010.06.025
    [8]
    邱祥风, 霍凯, 张新禹, 等. 一种基于雷达高分辨距离像的空天时敏目标识别方法[J]. 航空兵器, 2022, 29(2): 13–18. doi: 10.12132/ISSN.1673-5048.2020.0261

    QIU Xiangfeng, HUO Kai, ZHANG Xinyu, et al. A recognition method of aerospace time-sensitive targets based on radar high resolution range profile[J]. Aero Weaponry, 2022, 29(2): 13–18. doi: 10.12132/ISSN.1673-5048.2020.0261
    [9]
    郭宇. 基于高分辨距离像的支持向量数据描述目标识别算法研究[D]. [博士论文], 国防科技大学, 2018.

    GUO Yu. Research on support vector data description for HRRP-based target recognition[D]. [Ph. D. dissertation], National University of Defense Technology, 2018.
    [10]
    周云. 基于高分辨距离像的雷达目标识别研究[D]. [博士论文], 电子科技大学, 2016.

    ZHOU Yun. Research on radar target recognition based on high resolution range profile[D]. [Ph. D. dissertation], University of Electronic Science and Technology of China, 2016.
    [11]
    王彩云, 孔一荟. 基于稀疏表示字典优化的雷达高分辨距离像目标识别[J]. 南京航空航天大学学报, 2013, 45(6): 837–842. doi: 10.16356/j.1005-2615.2013.06.026

    WANG Caiyun and KONG Yihui. Radar high-resolution range profile target recognition based on sparse representation of dictionary optimized[J]. Journal of Nanjing University of Aeronautics &Astronautics, 2013, 45(6): 837–842. doi: 10.16356/j.1005-2615.2013.06.026
    [12]
    刘华林. 高分辨距离像雷达自动目标识别研究[D]. [博士论文], 电子科技大学, 2008.

    LIU Hualin. Research on radar automatic target recognition using high resolution range profiles[D]. [Ph. D. dissertation], University of Electronic Science and Technology of China, 2008.
    [13]
    PILCHER C M and KHOTANZAD A. Maritime ATR using classifier combination and high resolution range profile[J]. IEEE Transactions on Aerospace and Electronic Systems, 2011, 47(4): 2558–2573. doi: 10.1109/TAES.2011.6034651
    [14]
    SLOMKA S, GIBBINS D, GRAY D, et al. Features for high resolution radar range profile based ship classification[C]. The fifth International Symposium on Signal Processing and its Applications, Brisbane, Australia, 1999: 329–332.
    [15]
    杜兰, 刘宏伟, 保铮, 等. 一种利用目标雷达高分辨距离像幅度起伏特性的特征提取新方法[J]. 电子学报, 2005, 33(3): 411–415. doi: 10.3321/j.issn:0372-2112.2005.03.007

    DU Lan, LIU Hongwei, BAO Zheng, et al. A new feature extraction method using the amplitude fluctuation property of target HRRPs for radar automatic target recognition[J]. Acta Electronica Sinica, 2005, 33(3): 411–415. doi: 10.3321/j.issn:0372-2112.2005.03.007
    [16]
    KIM K T, SEO D K, and KIM H T. Efficient radar target recognition using the MUSIC algorithm and invariant features[J]. IEEE Transactions on Antennas and Propagation, 2002, 50(3): 325–337. doi: 10.1109/8.999623
    [17]
    ZHANG Xianda, SHI Yu, and BAO Zheng. A new feature vector using selected bispectra for signal classification with application in radar target recognition[J]. IEEE Transactions on Signal Processing, 2001, 49(9): 1875–1885. doi: 10.1109/78.942617
    [18]
    杜兰. 雷达高分辨距离像目标识别方法研究[D]. [博士论文], 西安电子科技大学, 2007.

    DU Lan. Study on radar HRRP target recognition[D]. [Ph. D. dissertation], Xidian University, 2007.
    [19]
    DU Lan, LIU Hongwei, WANG Penghui, et al. Noise robust radar HRRP target recognition based on multitask factor analysis with small training data size[J]. IEEE Transactions on Signal Processing, 2012, 60(7): 3546–3559. doi: 10.1109/TSP.2012.2191965
    [20]
    DU Lan, LIU Hongwei, BAO Zheng, et al. Radar HRRP target recognition based on higher order spectra[J]. IEEE Transactions on Signal Processing, 2005, 53(7): 2359–2368. doi: 10.1109/TSP.2005.849161
    [21]
    朱劼昊, 周建江, 吴杰. 基于半参数化概率密度估计的雷达目标识别[J]. 电子与信息学报, 2010, 32(9): 2161–2166. doi: 10.3724/SP.J.1146.2009.0120

    ZHU Jiehao, ZHOU Jianjiang, and WU Jie. Radar target recognition based on semiparametric density estimation[J]. Journal of Electronics &Information Technology, 2010, 32(9): 2161–2166. doi: 10.3724/SP.J.1146.2009.0120
    [22]
    崔姗姗. 基于概率统计模型的雷达目标HRRP识别[D]. [硕士论文], 南京航空航天大学, 2012.

    CUI Shanshan. Radar target recognition based on the statistic model of high resolution range profile[D]. [Master dissertation], Nanjing University of Aeronautics and Astronautics, 2012.
    [23]
    DU Lan, HE Hua, ZHAO Le, et al. Noise robust radar HRRP target recognition based on scatterer matching algorithm[J]. IEEE Sensors Journal, 2016, 16(6): 1743–1753. doi: 10.1109/JSEN.2015.2501850
    [24]
    SIM D G, KWON O K, and PARK R H. Object matching algorithms using robust Hausdorff distance measures[J]. IEEE Transactions on Image Processing, 1999, 8(3): 425–429. doi: 10.1109/83.748897
    [25]
    FENG Bo, DU Lan, LIU Hongwei, et al. Radar HRRP target recognition based on K-SVD algorithm[C]. 2011 IEEE CIE International Conference on Radar, Chengdu, China, 2011: 642–645.
    [26]
    FENG Bo, CHEN Bo, and LIU Hongwei. Radar HRRP target recognition with deep networks[J]. Pattern Recognition, 2017, 61: 379–393. doi: 10.1016/j.patcog.2016.08.012
    [27]
    XU Bin, CHEN Bo, WAN Jinwei, et al. Target-aware recurrent attentional network for radar HRRP target recognition[J]. Signal Processing, 2019, 155: 268–280. doi: 10.1016/j.sigpro.2018.09.041
    [28]
    WAN Jinwei, CHEN Bo, XU Bin, et al. Convolutional neural networks for radar HRRP target recognition and rejection[J]. EURASIP Journal on Advances in Signal Processing, 2019, 2019: 5. doi: 10.1186/s13634-019-0603-y
    [29]
    朱新奎. 空间目标高分辨距离像识别及微动特征提取方法研究[D]. [硕士论文], 西安电子科技大学, 2017.

    ZHU Xinkui. Research on space target HRRP recognition and micro-motion feature extraction methods[D]. [Master dissertation], Xidian University, 2017.
    [30]
    翟颖. 基于自编码模型的雷达高分辨距离像目标识别方法研究[D]. [硕士论文], 西安电子科技大学, 2018.

    ZHAI Ying. Study of radar high range resolution profiles target recognition based on auto-encoder[D]. [Master dissertation], Xidian University, 2018.
    [31]
    冯博. 雷达高分辨距离像特征提取与识别方法研究[D]. [博士论文], 西安电子科技大学, 2015.

    FENG Bo. Feature extraction and recognition methods for radar high range resolution profiles[D]. [Ph. D. dissertation], Xidian University, 2015.
    [32]
    DU Lan, WANG Penghui, ZHANG Lei, et al. Robust statistical recognition and reconstruction scheme based on hierarchical Bayesian learning of HRR radar target signal[J]. Expert Systems with Applications, 2015, 42(14): 5860–5873. doi: 10.1016/j.eswa.2015.03.029
    [33]
    DILOKTHANAKUL N, MEDIANO P A M, GARNELO M, et al. Deep unsupervised clustering with Gaussian mixture variational autoencoders[EB/OL]. https://doi.org/10.48550/arXiv.1611.02648, 2016.
    [34]
    CHEN Jian, DU Lan, and LIAO Leiyao. Discriminative mixture variational autoencoder for semisupervised classification[J]. IEEE Transactions on Cybernetics, 2022, 52(5): 3032–3046. doi: 10.1109/TCYB.2020.3023019
    [35]
    WEBB A R. Gamma mixture models for target recognition[J]. Pattern Recognition, 2000, 33(12): 2045–2054. doi: 10.1016/S0031-3203(99)00195-8
    [36]
    李斌, 姚康泽, 张银河, 等. 基于HRRP统计模型的目标识别[J]. 航天电子对抗, 2010, 26(3): 48–51. doi: 10.3969/j.issn.1673-2421.2010.03.014

    LI Bin, YAO Kangze, ZHANG Yinhe, et al. Target recognition based on the statistical model of high resolution range profile[J]. Aerospace Electronic Warfare, 2010, 26(3): 48–51. doi: 10.3969/j.issn.1673-2421.2010.03.014
    [37]
    PAN Mian, DU Lan, WANG Penghui, et al. Noise-robust modification method for Gaussian-based models with application to radar HRRP recognition[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(3): 558–562. doi: 10.1109/LGRS.2012.2213234
    [38]
    赵乃杰, 李辉, 金宝龙. 改进的雷达高分辨距离像统计识别方法[J]. 计算机工程与应用, 2012, 48(21): 118–122. doi: 10.3778/j.issn.1002-8331.2012.21.025.

    ZHAO Naijie, LI Hui, and JIN Baolong. Improved statistical identification method of high resolution range profiles[J]. Computer Engineering and Applications, 2012, 48(21): 118–122. doi: 10.3778/j.issn.1002-8331.2012.21.025.
    [39]
    DU Lan, LIU Hongwei, and BAO Zheng. Radar HRRP statistical recognition: Parametric model and model selection[J]. IEEE Transactions on Signal Processing, 2008, 56(5): 1931–1944. doi: 10.1109/TSP.2007.912283
    [40]
    SHI Lei, WANG Penghui, LIU Hongwei, et al. Radar HRRP statistical recognition with local factor analysis by automatic Bayesian Ying-Yang harmony learning[J]. IEEE Transactions on Signal Processing, 2011, 59(2): 610–617. doi: 10.1109/TSP.2010.2088391
    [41]
    PAN Mian, DU Lan, WANG Penghui, et al. Multi-task hidden Markov modeling of spectrogram feature from radar high-resolution range profiles[J]. EURASIP Journal on Advances in Signal Processing, 2012, 2012: 86. doi: 10.1186/1687-6180-2012-86
    [42]
    ZHANG Xuefeng, CHEN Bo, LIU Hongwei, et al. Infinite max-margin factor analysis via data augmentation[J]. Pattern Recognition, 2016, 52: 17–32. doi: 10.1016/j.patcog.2015.10.020
    [43]
    LI Liling, DU Lan, ZHANG Wei, et al. Enhancing information discriminant analysis: Feature extraction with linear statistical model and information-theoretic criteria[J]. Pattern Recognition, 2016, 60: 554–570. doi: 10.1016/j.patcog.2016.06.004
    [44]
    KINGMA D P and WELLING M. Auto-encoding variational Bayes[C]. 2nd International Conference on Learning Representations, Banff, Canada, 2014.
    [45]
    DU Chuan, CHEN Bo, XU Bin, et al. Factorized discriminative conditional variational auto-encoder for radar HRRP target recognition[J]. Signal Processing, 2019, 158: 176–189. doi: 10.1016/j.sigpro.2019.01.006
    [46]
    CHEN Wenchao, CHEN Bo, PENG Xiaojun, et al. Tensor RNN with Bayesian nonparametric mixture for radar HRRP modeling and target recognition[J]. IEEE Transactions on Signal Processing, 2021, 69: 1995–2009. doi: 10.1109/TSP.2021.3065847
    [47]
    GUO Dandan, CHEN Bo, CHEN Wenchao, et al. Variational temporal deep generative model for radar HRRP target recognition[J]. IEEE Transactions on Signal Processing, 2020, 68: 5795–5809. doi: 10.1109/TSP.2020.3027470
    [48]
    COPSEY K and WEBB A. Bayesian gamma mixture model approach to radar target recognition[J]. IEEE Transactions on Aerospace and Electronic systems, 2003, 39(4): 1201–1217. doi: 10.1109/TAES.2003.1261122
    [49]
    DU Lan, LIU Hongwei, BAO Zheng, et al. A two-distribution compounded statistical model for radar HRRP target recognition[J]. IEEE Transactions on Signal Processing, 2006, 54(6): 2226–2238. doi: 10.1109/TSP.2006.873534
    [50]
    赵峰, 张军英, 刘敬, 等. 基于Gamma-SLC混合密度估计的雷达目标识别[J]. 系统工程与电子技术, 2008, 30(3): 438–443. doi: 10.3321/j.issn:1001-506X.2008.03.012

    ZHAO Feng, ZHANG Junying, LIU Jing, et al. Radar target recognition based on the compounded density estimation of Gamma-SLC[J]. Systems Engineering and Electronics, 2008, 30(3): 438–443. doi: 10.3321/j.issn:1001-506X.2008.03.012
    [51]
    VAN TREES H L. Detection, Estimation, and Modulation Theory[M]. New York: John Wiley & Sons, 1971.
    [52]
    王鹏辉. 基于统计建模的雷达高分辨距离像目标识别方法研究[D]. [博士论文], 西安电子科技大学, 2012.

    WANG Penghui. Study of radar high resolution range profile target recognition based on statistical modeling[D]. [Ph. D. dissertation], Xidian University, 2012.
    [53]
    李晓辉, 黎湘, 郭桂蓉. 基于LDA算法的一维距离像特征提取[J]. 国防科技大学学报, 2005, 27(6): 72–76. doi: 10.3969/j.issn.1001-2486.2005.06.016

    LI Xiaohui, LI Xiang, and GUO Guirong. Feature extraction of HRRP based on LDA algorithm[J]. Journal of National University of Defense Technology, 2005, 27(6): 72–76. doi: 10.3969/j.issn.1001-2486.2005.06.016
    [54]
    DU Lan, CHEN Jian, HU Jing, et al. Statistical modeling with label constraint for radar target recognition[J]. IEEE Transactions on Aerospace and Electronic Systems, 2020, 56(2): 1026–1044. doi: 10.1109/TAES.2019.2925472
    [55]
    CHEN Jian, DU Lan, HE Hua, et al. Convolutional factor analysis model with application to radar automatic target recognition[J]. Pattern Recognition, 2019, 87: 140–156. doi: 10.1016/j.patcog.2018.10.014
    [56]
    CHEN Jian, DU Lan, and GUO Yuchen. Label constrained convolutional factor analysis for classification with limited training samples[J]. Information Sciences, 2021, 544: 372–394. doi: 10.1016/j.ins.2020.08.048
    [57]
    CHEN Minhua, SILVA J, PAISLEY J, et al. Compressive sensing on manifolds using a nonparametric mixture of factor analyzers: Algorithm and performance bounds[J]. IEEE Transactions on Signal Processing, 2010, 58(12): 6140–6155. doi: 10.1109/TSP.2010.2070796
    [58]
    CHEN Jian, LIAO Leiyao, ZHANG Wei, et al. Mixture factor analysis with distance metric constraint for dimensionality reduction[J]. Pattern Recognition, 2022, 121: 108156. doi: 10.1016/J.PATCOG.2021.108156
    [59]
    何珺田. 联合生成与判别模型的雷达HRRP目标识别方法研究[D]. [硕士论文], 西安电子科技大学, 2018.

    HE Juntian. Research on radar HRRP target recognition based on hybrid generative discriminative models[D]. [Master dissertation], Xidian University, 2018.
    [60]
    PEI Bingnan and BAO Zheng. Multi-aspect radar target recognition method based on scattering centers and HMMs classifiers[J]. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(3): 1067–1074. doi: 10.1109/TAES.2005.1541451
    [61]
    ZHU Feng, ZHANG Xianda, HU Yafeng, et al. Nonstationary hidden Markov models for multiaspect discriminative feature extraction from radar targets[J]. IEEE Transactions on Signal Processing, 2007, 55(5): 2203–2214. doi: 10.1109/TSP.2007.892708
    [62]
    潘勉, 王鹏辉, 杜兰, 等. 基于TSB-HMM模型的雷达高分辨距离像目标识别方法[J]. 电子与信息学报, 2013, 35(7): 1547–1554. doi: 10.3724/SP.J.1146.2012.01190

    PAN Mian, WANG Penghui, DU Lan, et al. Radar HRRP target recognition based on truncated stick-breaking hidden Markov model[J]. Journal of Electronics &Information Technology, 2013, 35(7): 1547–1554. doi: 10.3724/SP.J.1146.2012.01190
    [63]
    LIAO Leiyao, DU Lan, and CHEN Jian. Class factorized complex variational auto-encoder for HRR radar target recognition[J]. Signal Processing, 2021, 182: 107932. doi: 10.1016/j.sigpro.2020.107932
    [64]
    陈健. 基于概率统计模型的雷达高分辨距离像目标识别方法研究[D]. [博士论文], 西安电子科技大学, 2020.

    CHEN Jian. Study of radar HRRP target recognition based on probability statistical model[D]. [Ph. D. dissertation], Xidian University, 2020.
    [65]
    YUKSEL S E, WILSON J N, and GADER P D. Twenty years of mixture of experts[J]. IEEE Transactions on Neural Networks and Learning Systems, 2012, 23(8): 1177–1193. doi: 10.1109/TNNLS.2012.2200299
    [66]
    CHEN Wenchao, CHEN Bo, LIU Yicheng, et al. Bidirectional recurrent gamma belief network for HRRP target recognition[J]. Signal Processing, 2021, 188: 108213. doi: 10.1016/j.sigpro.2021.108213
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML
    Article views(2926) PDF downloads(537) Cited by()
    Proportional views
    Related

    京ICP备20021838号-8   Supported by: Beijing Renhe Information Technology Co. Ltd     百度统计

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return