Volume 12 Issue 5
Oct.  2023
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Article Navigation >  Journal of Radars  > 2023  >  12(5): 1014-1030
Zhou Yu, Wang Hai-peng, Chen Si-zhe. SAR Automatic Target Recognition Based on Numerical Scattering Simulation and Model-based Matching[J]. Journal of Radars, 2015, 4(6): 666-673. doi: 10.12000/JR15080
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
shu

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

DOI: 10.12000/JR22245
  • 1.

    Department of Electronic Engineering, Tsinghua University, Beijing 100084, China

  • 2.

    Shenzhen MSU-BIT University, Shenzhen 518172, China

  • 3.

    Guangdong Laboratory of Machine Perception and Intelligent Computing, Shenzhen 518172, China

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
    Abstract
  • 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.

     

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