Radar-Based Human Target Detection using Deep Residual U-Net for Smart Home Applications

We present a radar-based detection processing framework for accurate detection and counting of human targets in an indoor environment. This can be used to control lighting, heating, ventilation and air conditioning (HVAC) in smart homes and other presence related loads in commercial, office, and public spaces. Such smart home applications can facilitate monitoring, controlling, and saving energy. Conventionally, the radar range-Doppler processing pipeline includes moving target indicators (MTI) to remove static targets, maximal ratio combining (MRC) to integrate data across antennas, constant false alarm rate (CFAR) based detectors and then clustering algorithms to generate the target range-Doppler detections. However, the conventional pipeline suffers from ghost targets and multi-path reflections from static objects such as walls, furniture, etc. Further, conventional parametric clustering algorithms lead to single target splits and adjacent target merges in the target range-Doppler detections. To overcome such issues, we propose a deep residual U-net architecture that generates human target detections directly from static target removed range-Doppler images (RDI). To train this network, we record RDIs from a variety of indoor scenes with different configurations and multiple humans targets. We devise a custom loss function and apply augmentation strategies to generalize this model during real-time inference of the model. We demonstrate that the proposed network can efficiently learn to detect and correctly count human targets under different indoor environments while the conventional signal processing pipeline fails.