Compressed Sensing-Based Obstacle Detection for Future Urban Air Mobility Scenarios

To enable a safe and reliable operation in future urban air mobility scenarios with a large number of autonomous aerial vehicles, automatic sensing systems will be required during the starting and landing phase, as well as through comparatively narrow air spaces typical for urban areas. This letter shows an implementation of a compressive sensing (CS)-based obstacle-detection and direction-of-arrival (DoA) estimation approach. To this end, a multiple-input multiple-output (MIMO) frequency-modulated continuous wave radar system with two transmit and four receive antenna elements is employed to sense the environment and provide situational awareness from a virtual aperture with eight antennas. Example measurements result from a typical urban air setting with a priori unknown sparsity. The results show that greater signal dynamics are attained from CS compared to conventional beamforming-based DoA estimation techniques. Furthermore, measurements are verified by corresponding time-synchronous LiDAR measurements. CS-based obstacle detection and DoA estimation is shown to work, even in complex measurement environments.