{"ID":2873479,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.06751","arxiv_id":"2509.06751","title":"RadHARSimulator V1: Model-Based FMCW Radar Human Activity Recognition Simulator","abstract":"Radar-based human activity recognition (HAR) is a pivotal research area for applications requiring non-invasive monitoring. However, the acquisition of diverse and high-fidelity radar datasets for robust algorithm development remains a significant challenge. To overcome this bottleneck, a model-based frequency-modulated continuous wave (FMCW) radar HAR simulator is developed. The simulator integrates an anthropometrically scaled $13$-scatterer kinematic model to simulate $12$ distinct activities. The FMCW radar echo model is employed, which incorporates dynamic radar cross-section (RCS), free-space or through-the-wall propagation, and a calibrated noise floor to ensure signal fidelity. The simulated raw data is then processed through a complete pipeline, including moving target indication (MTI), bulk Doppler compensation, and Savitzky-Golay denoising, culminating in the generation of high-resolution range-time map (RTM) and Doppler-time maps (DTMs) via both short-time Fourier transform (STFT) and Fourier synchrosqueezed transform (FSST). Finally, a novel neural network method is proposed to validate the effectiveness of the radar HAR. Numerical experiments demonstrate that the simulator successfully generates high-fidelity and distinct micro-Doppler signature, which provides a valuable tool for radar HAR algorithm design and validation. The installer of this simulator is released at: https://github.com/JoeyBGOfficial/RadHARSimulatorV1-Model-Based-FMCW-Radar-Human-Activity-Recognition-Simulator.","short_abstract":"Radar-based human activity recognition (HAR) is a pivotal research area for applications requiring non-invasive monitoring. However, the acquisition of diverse and high-fidelity radar datasets for robust algorithm development remains a significant challenge. To overcome this bottleneck, a model-based frequency-modulate...","url_abs":"https://arxiv.org/abs/2509.06751","url_pdf":"https://arxiv.org/pdf/2509.06751v2","authors":"[\"Weicheng Gao\"]","published":"2025-09-08T14:40:29Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[]","has_code":false,"code_links":[{"ID":610056,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2873479,"paper_url":"https://arxiv.org/abs/2509.06751","paper_title":"RadHARSimulator V1: Model-Based FMCW Radar Human Activity Recognition Simulator","repo_url":"https://github.com/JoeyBGOfficial/RadHARSimulatorV1-Model-Based-FMCW-Radar-Human-Activity-Recognition-Simulator","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}