Enhanced Indoor Multi-Target Counting Based on Micro-Doppler Features with 24GHz Radar

The benefits demonstrated in smart home and energy-saving applications have sparked considerable interest in non-contact technologies for detecting indoor human presence. In opti-mizing energy utilization, precise remote assessment of individual count is of paramount importance.However, the presence of multiple human targets in indoor environments poses a challenge for quantity estimation, especially when using a simple one-transmitter-two-receiver 24GHz radar system. Due to limitations in bandwidth and the number of receiving antennas, the system exhibits poor resolution, rendering traditional distance-angle estimation algorithms ineffective in distinguishing targets.To address the indistinguishability issue of multiple targets in the distance-angle domain, we propose a target counting algorithm based on the micro-Doppler domain. Leveraging an atomic norm minimization method, this approach separates target respiratory frequencies and determines the number of targets. Through simulations and practical experiments, our approach demonstrates superior performance in target counting compared to traditional Fourier transform and compressive sensing methods.