Low cost automotive sensors are in common use to support assisted driving: e.g. object detection and classification for collision avoidance.
This project is to investigate the suitability of commercial low-cost automotive radar sensors to the specific airborne application area: e.g. use on manned and unmanned airborne platforms for collision avoidance, sense and avoid, landing in degraded visual environments.
Automotive and airborne applications have similar challenges: detection of hazards from a moving vehicle with sufficient range and robustness to permit safe decisions. But airborne has its own challenges: full range of platform and attitudes, look down to high clutter backgrounds, and a different set of objects/hazards of interest.
The following TI1642 evaluation kit is a complete 77GHz 2x4 MIMO radar system on a board http://www.ti.com/tool/AWR1642BOOST
This connects to PC via USB, and contains tools for data recording, data visualisation, and libraries for object detection and clustering etc.
See https://training.ti.com/short-range-radar-demonstration-object-clustering-and-tracking for a video of object clustering and tracking.
The approach to the project will be to mount this device on a tower and collect data with the following targets in view: ground obstacles, wires, pylons, vehicles, people, drones etc. This will allow us to characterize if these types of automotive radar systems can operate in the airborne environment and can detect and track specific airborne related hazards. The study will also investigate what changes would be necessary to adapt automotive object detection, classification and tracking algorithms to airborne applications. This will require offline data analysis and signal processing using e.g. Matlab or Python.
If time permits, the low weight sensor could be installed on a drone to collect sensor data for offline processing during flight and landing.
Birte Kronbak Andersen is not a possible supervisor, but DTU supervisor can be found at either DTU Space or DTU Electro.
Specialist Brian Tollins
Research & Innovation Management
Technology & Innovation
I samarbejde medTERMA A/S
Digital Signal Processing, Radar Theory. Experience with SDR devices, radar hardware and lab measurements is desirable, but not essential.