Ensemble Learning With CNN and Wavelet-LDA Features for Micro-Doppler-Based Automotive Target Classification

The micro-Doppler effect refers to the modulation of radar signals caused by the motion of objects with internal moving parts, such as rotating blades, oscillating components, or vibrating structures. This effect can be utilized in various applications such as target recognition and classification of moving objects. Automotive radar target classification, using micro-Doppler signatures, has become a crucial technique for enhancing advanced driver assistance systems (ADAS) and autonomous driving capabilities. Micro-Doppler signatures arise from small-scale movements such as the rotation of wheels, pedestrian’s arms swinging, pedals and wheels rotation of bicycle or vibration of objects superimposed on the primary Doppler shift caused by the object’s overall motion. Effective classification improves object detection accuracy, enhances safety and helps in predictive decision-making for autonomous driving systems. This paper presents a lightweight and efficient framework for six-class classification by combining shallow learning and ensemble methods. A single-layer CNN with only four filters is used for feature extraction, followed by wavelet transform for dimension reduction instead of traditional pooling. Further reduction and class separability are achieved using 2D Linear Discriminant Analysis. Finally, an ensemble of 200 decision trees is employed for classification. This hybrid approach offers a novel alternative to deep models, balancing performance with low computational cost. Simulation results show that the proposed method has 97.33% classification accuracy with 30dB signal to noise ratio (SNR), which outperforms existing deep learning models in terms of classification accuracy and computational efficiency, demonstrating its effectiveness for automotive radar target classification.