Resource-efficient Deep Neural Networks for Automotive Radar Interference Mitigation. (arXiv:2201.10360v1 [eess.SP])

Web: http://arxiv.org/abs/2201.10360

Radar sensors are crucial for environment perception of driver assistance
systems as well as autonomous vehicles. With a rising number of radar sensors
and the so far unregulated automotive radar frequency band, mutual interference
is inevitable and must be dealt with. Algorithms and models operating on radar
data are required to run the early processing steps on specialized radar sensor
hardware. This specialized hardware typically has strict resource-constraints,
i.e. a low memory capacity and low computational power. Convolutional Neural
Network …

arxiv automotive deep networks neural neural networks