A target recognition algorithm using machine learning based on millimeter wave radar on intelligent connected vehicles

Abstract Recognition technology based on millimeter wave radar (MMW) can operate in all-weather conditions and has received much attention in the field of intelligent connected vehicles (ICVs). However, the label information of the targets cannot be directly obtained from the original radar point clouds, making it necessary to develop advanced recognition algorithms. This paper proposes a target recognition algorithm based on machine learning that utilizes radar point clouds and leverages the radar reflection intensity to improve target recognition accuracy. Firstly, regional division and density clustering techniques are employed to preprocess the original point clouds from the MMW and segment them into meaningful regions, thereby reducing the computational burden; Secondly, relevant features are extracted from the processed radar point cloud, including radar scattering cross section and its related features. Finally, to improve target recognition accuracy, this paper proposes a grid search optimization principal component analysis support vector machine (GS-PCA-SVM) classification algorithm. The algorithm uses PCA to reduce the dimensionality of the data while preserving key information; then, it optimizes the parameters and kernel function of SVM by using the GS method to improve the performance of the classifier. The experimental results indicate that the recognition algorithm proposed in this paper achieves accuracies of 80%, 93%, and 95% on static, dynamic, and mixed datasets, respectively. Real vehicle experiments also prove that this algorithm has high accuracy and reliability when applied to ICV.