基于PCA-LDA-SVM的多普勒雷达车型识别算法

车辆检测和车型识别是智能交通系统(Intelligent transportation system,ITS)中的一个重要方面,而目标识别是低分辨率雷达领域的一个难点.该文提出一种用多普勒雷达进行车型识别的方法,把车辆建模成包含多个散射中心的目标体,散射中心与雷达的距离与频谱能量有关,因此同一目标的频谱变化反映了该目标长高等轮廓特征.然后将有效的频谱特征结合主成分分析(Principal component and analysis,PCA)和线性判别分析(Linear discriminant analysis,LDA)进行降维,再利用支持向量机(Support vector machine,SVM)等分类器实现分型.文章对不同识别算法交叉验证的实验结果进行比较,表明基于PCA-LDA-SVM的车型识别算法效果理想,有广泛的应用前景.

Vehicle Recognition algorithm with Doppler Radar Based on PCA-LDA-SVM

Vehicle detection and recognition is important to the development of intelligent transportation system(ITS),but target recognition is a challenging problem for low-resolution radar.Hence,a vehicle recognition approach using Doppler radar is proposed,and the spectrum variation of one vehicle reflects its outline.Then,the dimension of effective spectrum feature can be reduced by the methods of principal component analysis(PCA) and linear discriminant analysis (LDA).Vehicles can be classified into three types by classifier algorithms such as support vector machine(SVM),K-nearest neighbor(KNN).Finally,experimental results of different algorithms are compared by cross validation,and it shows that the algorithm based on PCA-LDA-SVM can achieve an ideal result.