Data rate reduction for chirp-sequence based automotive radars using compressed sensing

Abstract

For autonomous driving high-resolution radar sensors are key components, which have the drawback of high data rates. In order to reduce the amount of sampled data, random samples can be omitted and afterwards reconstructed using compressed sensing methods. A possible application is that not every receiving antenna element demands its own analog-to-digital converter. One converter can be used for several receiving elements with a random assignment to each antenna instead. In this paper, an analysis is presented of how many samples can be neglected such that a successful reconstruction in post-processing for an automotive scenario is possible. A measurement result is shown to prove that with only 40% of samples a successful reconstruction is possible.