ChirpNet: Noise-Resilient Sequential Chirp Based Radar Processing for Object Detection

Radar-based object detection (OD) requires extensive pre-processing and complex Machine Learning (ML) pipelines. Previous approaches have attempted to address these challenges by processing raw radar data frames directly from the ADC or through FFT-based post-processing. However, the input data requirements and model complexity continue to impose significant computational overhead on the edge system. In this work, we introduce ChirpNet, a noise-resilient and efficient radar processing ML architecture for object detection. Diverging from previous approaches, we directly handle raw ADC data from multiple antennas per chirp using a sequential model, resulting in a substantial 15 × reduction in complexity and a 3 × reduction in latency, while maintaining competitive OD performance. Furthermore, our proposed scheme is robust to input noise variations compared to prior works.