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

Abstract

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-todigital 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.

Effort Considerations of Compressed Sensing for Automotive Radar

“…The first step in the signal processing chain is to determine the positions of missing samples. In the case of data rate reduction as in [8], the positions are known due to the reduction scheme. For the interference mitigation the positions of interfered samples are not known.…”

“…This level is the threshold (thr), which is lowered in consecutive iterations to consider further frequencies. As already mentioned in [8], it is important to ensure that none of those artefacts are used for reconstruction as they would be intensified and considered as a target.…”

“…Instead, the second scenario is used to show the influence of the required iteration number for a successful reconstruction. As in [8] an extended target vehicle is simulated consisting of several close scattering centres in range and velocity with different power levels. The data rate is reduced to one third and the missing samples are reconstructed afterwards.…”

“…The data rate is reduced to one third and the missing samples are reconstructed afterwards. In this example, a reconstruction of over 90 % of the targets is achieved in [8]. To select a more challenging scenario, complete ramps are omitted and not only random samples as this results in much higher artefact levels [8].…”

“…The affected data are detected and reconstructed as introduced in [6]. In the second example the data rate of the radar signal is reduced as shown in [7] and [8].…”