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

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

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。

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.