FMCW radar

This dataset contains raw FMCW radar signals collected for human localization and activity monitoring in indoor environments. The data was recorded using mmWave radar sensors across two different laboratory settings, designed to simulate real-life scenarios for human detection and localization tasks.

This paper proposes a novel Recursive Convolutional Target Detector (RCTD) for Frequency-Modulated Continuous-Wave (FMCW) radar in complex automotive scenarios. Leveraging a lightweight convolutional neural network, RCTD efficiently localizes multiple targets despite strong interference. Detailed simulations and a hardware prototype on an FPGA-based deep learning processor demonstrate real-time feasibility, low false alarm rates, and higher detection accuracy under stringent resource constraints.

For gesture recognition, radar sensors provide a unique alternative to other input devices, such as cameras or motion sensors. They combine a low sensitivity to lighting conditions, an ability to see through surfaces, and user privacy preservation, with a small form factor and low power usage. However, radar signals can be noisy, complex to analyze, and do not transpose from one radar to another.

The development in the automation sector and aircraft design has enabled enormous innovations in the urban aviation sector, which includes Advanced Air Mobility (AAM). AAM includes the on-demand air transportation of goods and passengers using the drone between aerodromes within Air Corridors. Air Corridors are an integral part of AAM, a performance-based controlled airspace where drones follow a specific set of protocols. This includes appointing the Skylane, route availability, traffic control, collision avoidance, etc.