Grant: Alliance Grants

Radar systems are highly utilized today in automobile industry for driver assistance and autonomous driving systems due to their high reliability and weather independencies. Digital signal and image processing is an integral part of radar systems to detect and classify objects from the sensors and collected data. Typically, frequency modulated continuous wave radar is used for automotive applications. This system employs constant false alarm rate algorithms for object detection in the receiver after a Fast Fourier Transform calculation. These algorithms depend on estimation of background noise, which in the case of a multiple object scenario gives inaccurate results and thus leads to degraded detection accuracy.In this proposed collaboration with Karlsruhe University of Applied Sciences, we aim to detect and classify objects from radar data utilizing machine learning and artificial neural networks. Using convolutional neural networks (CNNs), features will be extracted from the radar signal frequency spectra to accurately detect objects and classify them based on their signal patterns. The goal is to develop multidimensional CNNs capable of processing the Fourier spectra of 4D radar signals to improve the resolution and classification capability of the radar system. In the proposed project, the applicant is expected to train HQPs in high-demand areas of digital signal processing utilizing artificial intelligence and machine learning, which will drive innovation in emerging areas of research and development in radar systems, specifically in the auto industry. This project allows for strengthening our international collaborations and will provide a platform for an Alliance International Collaboration grant application. In addition, the developed algorithms could be integrated into an embedded system in future for various industrial applications in Canada and Germany.