4D imaging radar is rapidly emerging as a core technology for autonomous driving, delivering LiDAR-level performance at a fraction of the cost. Yet, the inherent sparsity and noise of radar data have long limited the performance of conventional deep learning models. RAPA is designed to overcome these limitations. This software-defined, 360-degree perception solution — powered exclusively by multiple 4D imaging radars — learns the unique characteristics of radar signals and applies optimized filtering. Using an attention-based deep learning model trained on our proprietary dataset, RAPA achieves real-time, high-precision object detection and tracking. By leveraging Doppler velocity, RAPA accurately distinguishes between static and dynamic objects, achieving over 40% higher accuracy than competing solutions on public benchmarks. Operating efficiently on edge embedded platforms, RAPA delivers a cost-effective and scalable solution for autonomous vehicles, USVs (Unmanned Surface Vehicles), and robotics — representing a major leap forward in radar-only perception technology.