Target Classification Based on Micro-Doppler Signatures

Micro-Doppler signatures hold significant application value in the field of target classification. When radar waves illuminate a target, subtle motions on the target's surface (such as limb movements during human walking or rotating components of vehicles) generate unique frequency modulation phenomena, known as the micro-Doppler effect. These signatures contain fine motion information about the target, providing crucial basis for classification and recognition.

Target classification based on micro-Doppler features typically involves three core steps: signal preprocessing, feature extraction, and classification modeling. During signal preprocessing, raw radar echoes require denoising and time-frequency analysis, commonly implemented using Short-Time Fourier Transform (STFT) or Wavelet Transform algorithms to obtain clear time-frequency distribution plots. In the feature extraction phase, discriminative micro-Doppler characteristics are mined from the time-frequency domain, such as instantaneous frequency, bandwidth, and periodicity patterns. Finally, machine learning or deep learning algorithms are employed to build classification models that map extracted features to specific target categories, with common implementations including Support Vector Machines (SVM) for traditional approaches or Convolutional Neural Networks (CNN) for image-like time-frequency representations.

This technology shows broad prospects in security surveillance, autonomous driving, and military reconnaissance applications. By analyzing micro-Doppler fingerprints of different targets, systems can accurately distinguish between humans, vehicles, or drones while maintaining high classification accuracy even in complex environments.