SMOTE-Diffusion: A Combined Approach for Authentic Data Generation for Time-Domain Radar Signal in Intelligent Transportation System

Interest in radar-based object recognition within intelligent transportation systems (ITSs) is surging, attributed to its robustness against environmental variations and inherent privacy-preserving capabilities. Among radar technologies, frequency-modulated continuous-wave (FMCW) radar is well-studied and excels at classifying moving objects through the frequency-domain analysis and the Doppler effect. However, it struggles with classifying stationary objects. In contrast, impulse radio ultrawideband (IR-UWB) radar transmits ultrashort pulses over a wide bandwidth, offering high temporal resolution for the time-domain analysis to classify stationary objects. It can also perform frequency-domain analysis based on the Doppler effect using pulse repetition frequency (PRF). Despite these advantages, the time-domain analysis for stationary object classification remains limited due to data scarcity and complex signal processing. This study proposes diffusion model with synthetic minority over-sampling technique (SMOTE-Diffusion) to address these challenges. SMOTE-Diffusion generates high-quality time-domain data that reflect the characteristics of stationary objects, reducing data collection burdens while ensuring diverse and realistic datasets. The generated data were evaluated using quantitative metrics, such as structural similarity index measure (SSIM), mean squared error (mse), and mean absolute error (MAE), as well as qualitative methods such as principal component analysis (PCA). Applied to classification models, the augmented dataset significantly improved the average performance compared with the nonaugmented data. These results demonstrate SMOTE-Diffusion’s ability to generate realistic radar data by capturing the distribution characteristics of real datasets. Therefore, by addressing the challenges associated with detecting stationary objects, it lays the foundation for developing integrated models that can classify both stationary and moving objects using IR-UWB radar.