Volume 12 Issue 5
Oct.  2023
Turn off MathJax
Article Contents
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

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

doi: 10.12000/JR22245
Funds:  The National Natural Science Foundation of China (62101304, 61925106), Research and development project commissioned by Huawei Technologies Co. LTD
More Information
  • Corresponding author: ZHAO Zhichun, zzc@smbu.edu.cn
  • Received Date: 2022-12-31
  • Rev Recd Date: 2023-02-22
  • Available Online: 2023-02-28
  • Publish Date: 2023-03-13
  • In radar-based road target recognition, the increase in target feature dimension is a common technique to improve recognition performance when targets become diverse, but their characteristics are similar. However, the increase in feature dimension leads to feature redundancy and dimension disasters. Therefore, it is necessary to optimize the extracted high-dimensional feature set. The Adaptive Genetic Algorithm (AGA) based on random search is an effective feature optimization method. To improve the efficiency and accuracy of the AGA, the existing improved AGA methods generally utilize the prior correlation between features and targets for pre-dimensionality reduction of high-dimensional feature sets. However, such algorithms only consider the correlation between a single feature and a target, neglecting the correlation between feature combinations and targets. The selected feature set may not be the best recognition combination for the target. Thus, to address this issue, this study proposes an improved AGA via pre-dimensionality reduction based on Histogram Analysis (HA) of the correlation between different feature combinations and targets. The proposed method can simultaneously improve the efficiency and accuracy of feature selection and target recognition performance. Comparative experiments based on a real dataset of the millimeter-wave radar showed that the average accuracy of target recognition of the proposed HA-AGA method could reach 95.7%, which is 1.9%, 2.4%, and 10.1% higher than that of IG-GA, ReliefF-IAGA, and improved RetinaNet methods, respectively. Comparative experiments based on the CARRADA dataset showed that the average accuracy of target recognition of the proposed HA-AGA method could reach 93.0%, which is 1.2% and 1.5% higher than that of IG-GA and ReliefF-IAGA methods, respectively. These results verify the effectiveness and superiority of the proposed method compared with existing methods. In addition, the performance of different feature optimization methods coupled with the integrated bagging tree, fine tree, and K-Nearest Neighbor (KNN) classifier was compared. The experimental results showed that the proposed method exhibits evident advantages when coupled with different classifiers and has broad applicability.

     

  • loading
  • [1]
    PATOLE S M, TORLAK M, WANG Dan, et al. Automotive radars: A review of signal processing techniques[J]. IEEE Signal Processing Magazine, 2017, 34(2): 22–35. doi: 10.1109/MSP.2016.2628914
    [2]
    周刚, 吴杰. 汽车防撞毫米波雷达系统参数优化设计[J]. 电讯技术, 2011, 51(7): 77–80. doi: 10.3969/j.issn.1001-893x.2011.07.016

    ZHOU Gang and WU Jie. Parameters optimized design of automobile anti-collision millimeter wave radar system[J]. Telecommunication Engineering, 2011, 51(7): 77–80. doi: 10.3969/j.issn.1001-893x.2011.07.016
    [3]
    元志安, 周笑宇, 刘心溥, 等. 基于RDSNet的毫米波雷达人体跌倒检测方法[J]. 雷达学报, 2021, 10(4): 656–664. doi: 10.12000/JR21015

    YUAN Zhian, ZHOU Xiaoyu, LIU Xinpu, et al. Human fall detection method using millimeter-wave radar based on RDSNet[J]. Journal of Radars, 2021, 10(4): 656–664. doi: 10.12000/JR21015
    [4]
    ZHAO Yan, ZHAO Lingjun, XIONG Boli, et al. Attention receptive pyramid network for ship detection in SAR images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 2738–2756. doi: 10.1109/JSTARS.2020.2997081
    [5]
    MIAO Tian, ZENG Hongcheng, YANG Wei, et al. An improved lightweight RetinaNet for ship detection in SAR images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15: 4667–4679. doi: 10.1109/JSTARS.2022.3180159
    [6]
    WANG Chenxi, CHEN Zhichao, CHEN Xin, et al. Detection of MMW radar target based on Doppler characteristics and deep learning[C]. IEEE International Conference on Artificial Intelligence and Industrial Design, Guangzhou, China, 2021: 266–271.
    [7]
    ZHANG Tianwen, ZHANG Xiaoling, KE Xiao, et al. HOG-ShipCLSNet: A novel deep learning network with HOG feature fusion for SAR ship classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5210322. doi: 10.1109/TGRS.2021.3082759
    [8]
    SOROWKA P and ROHLING H. Pedestrian classification with 24 GHz chirp sequence radar[C]. 2015 16th International Radar Symposium, Munich, Germany, 2015: 167–173.
    [9]
    余月琴. 车载毫米波雷达行人识别算法研究[D]. [硕士论文], 电子科技大学, 2020: 11–15.

    YU Yueqin. Research on pedestrian recognition algorithm of automobile millimeter wave radar[D]. [Master dissertation], University of Electronic Science and Technology of China, 2020: 11–15.
    [10]
    HEUEL S and ROHLING H. Pedestrian classification in automotive radar systems[C]. 2012 13th International Radar Symposium, Warsaw, Poland, 2012: 39–44.
    [11]
    LANDGREBE D A. Signal Theory Methods in Multispectral Remote Sensing[M]. Hoboken, USA: Wiley, 2003.
    [12]
    EL AKADI A, AMINE A, EL OUARDIGHI A E, et al. A two-stage gene selection scheme utilizing MRMR filter and GA wrapper[J]. Knowledge and Information Systems, 2011, 26(3): 487–500. doi: 10.1007/s10115-010-0288-x
    [13]
    TEKELI B, GURBUZ S Z, and YUKSEL M. Information-theoretic feature selection for human Micro-Doppler signature classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(5): 2749–2762. doi: 10.1109/TGRS.2015.2505409
    [14]
    JOLLIFFE I T. Principal Component Analysis[M]. 2nd ed. New York, USA: Springer, 2002: 1–9.
    [15]
    WANG Zigeng, XIAO Xia, and RAJASEKARAN S. Novel and efficient randomized algorithms for feature selection[J]. Big Data Mining and Analytics, 2020, 3(3): 208–224. doi: 10.26599/BDMA.2020.9020005
    [16]
    GÜRBÜZ S Z, TEKELI B, KARABACAK C, et al. Feature selection for classification of human Micro-Doppler[C]. IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems, Tel Aviv, Israel, 2013: 1–5.
    [17]
    WU Yanwei, JIANG Mian, PEI Xiaoshuai, et al. Feature selection and decision fusion methods in target recognition[C]. IET International Radar Conference, Chongqing, China, 2021: 687–690.
    [18]
    XIAO Peng, WANG Zigeng, and RAJASEKARAN S. Novel speedup techniques for parallel singular value decomposition[C]. 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems, Exeter, UK, 2018: 188–195.
    [19]
    CHANDRASHEKAR G and SAHIN F. A survey on feature selection methods[J]. Computers & Electrical Engineering, 2014, 40(1): 16–28. doi: 10.1016/j.compeleceng.2013.11.024
    [20]
    WANG Yunyan, ZHUO Tong, ZHANG Yu, et al. Hierarchical polarimetric SAR image classification based on feature selection and Genetic algorithm[C]. 2014 12th International Conference on Signal Processing (ICSP), Hangzhou, China, 2014: 764–768.
    [21]
    BHANU B and LIN Yingqiang. Genetic algorithm based feature selection for target detection in SAR images[J]. Image and Vision Computing, 2003, 21(7): 591–608. doi: 10.1016/S0262-8856(03)00057-X
    [22]
    SENTHILNATH J, OMKAR S N, MANI V, et al. Multi-sensor satellite remote sensing images for flood assessment using swarm intelligence[C]. 2015 International Conference on Cognitive Computing and Information Processing, Noida, India, 2015: 1–5.
    [23]
    LIANG Kui, DAI Wei, and DU Rui. A feature selection method based on improved genetic algorithm[C]. 2020 Global Reliability and Prognostics and Health Management (PHM-Shanghai), Shanghai, China, 2020: 1–5.
    [24]
    LI Yinghao, SHI Kai, QIAO Fuqiang, et al. A feature subset selection method based on the combination of PCA and improved GA[C]. 2020 2nd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), Taiyuan, China, 2020: 191–194.
    [25]
    YANG B S, HAN Tian, and YIN Zhongjun. Fault diagnosis system of induction motors using feature extraction, feature selection and classification algorithm[J]. JSME International Journal Series C:Mechanical Systems, Machine Elements and Manufacturing, 2006, 49(3): 734–741. doi: 10.1299/jsmec.49.734
    [26]
    LIU Ming, DING Xiangqian, YU Shusong, et al. Research on feature selection in near-infrared spectroscopy classification based on improved adaptive genetic algorithm combined with ReliefF[C]. 2017 9th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), Hangzhou, China, 2017: 403–406.
    [27]
    YANG Jianyong and YAN Ruqiang. A multidimensional feature extraction and selection method for ECG arrhythmias classification[J]. IEEE Sensors Journal, 2021, 21(13): 14180–14190. doi: 10.1109/JSEN.2020.3047962
    [28]
    THEJASWEE M, SRILAKSHMI P, KARUNA G, et al. Hybrid IG and GA based feature selection approach for text categorization[C]. 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, 2020, 1606–1613.
    [29]
    DELON J, DESOLNEUX A, LISANI J L, et al. A nonparametric approach for histogram segmentation[J]. IEEE Transactions on Image Processing, 2007, 16(1): 253–261. doi: 10.1109/TIP.2006.884951
    [30]
    OUAKNINE A, NEWSON A, REBUT J, et al. CARRADA dataset: Camera and automotive Radar with range- angle- Doppler annotations[C]. 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy, 2021: 5068–5075.
    [31]
    WHITLEY D. A genetic algorithm tutorial[J]. Statistics and Computing, 1994, 4(2): 65–85. doi: 10.1007/BF00175354
    [32]
    SAITO T and REHMSMEIER M. The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets[J]. PLoS One, 2015, 10(3): e0118432. doi: 10.1371/journal.pone.0118432
  • 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
    Article views(655) PDF downloads(175) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint