Radar is a key technology in automotive driving for target detection and perception. In this work, we leverage prior environmental information in the form of occupancy maps to design space-time codes for a fully digital MIMO radar. We transform this design problem into the optimization of spatial beamforming gains and time-domain codes. The beamforming gains are optimized to enhance the strength of returns from cells associated with a higher uncertainty of occupancy. The timedomain codes are optimized to minimize the correlation between returns of targets within the drivable space. We validate our method on the nuScenes dataset to show that the designed spacetime codes achieve higher detection rates than designs that do not rely on prior information from occupancy maps.

Millimeter-wave radar is a common sensor modality used in automotive driving for target detection and perception. These radars can benefit from side information on the environment being sensed, such as lane topologies or data from other sensors. Existing radars do not leverage this information to adapt waveforms or perform prior-aware inference. In this paper, we model the side information as an occupancy map and design transmit beamformers that are customized to the map. Our method maximizes the probability of detection in regions with a higher uncertainty on the presence of a target. Simulation results on the nuScenes dataset show that the designed beamformer achieves substantially higher detection rates than a conventional omnidirectional beamformer for the same transmitted power.

In recent years, convolutional neural networks (CNNs) have been increasingly used for classifying radar micro-Doppler signatures of various targets. However, obtaining large amounts of data for efficient CNN training in defence and surveillance scenarios can be challenging. Therefore, designing techniques that maximize the use of available samples is critical. In this paper, we propose an approach built on the hypothesis that certain classes of radar spectrograms, such as those used for discerning armed from unarmed walking personnel, do not have information about the class encoded in the trajectory. Therefore, our method entails segmenting each input spectrogram into individual frames that correspond to a distinct step of human locomotion. Subsequently, we classify each segment independently and combine the resulting classification scores to obtain the final score for the entire spectrogram. As a result of this segmentation, the size of the training set is increased, whereas the dimensions of each sample - and therefore the number of parameters in the classifier - is decreased, reducing the risk of overfitting. Our experimental results demonstrate the effectiveness of our approach and its potential to enhance CNN-based classification of micro-Doppler signatures.