Abstract: A radar device includes a radar front end configured to send radar signals and to receive reflected radar signals, processing circuitry configured to provide digital radar data based on the received reflected radar signals, and a digital filter configured to process the digital radar data to obtain information about objects which reflected the radar signals. The device further comprises machine learning logic with a policy network configured to set the parameters of the digital filter based on the digital radar data, and a reward value generating network including a plurality of heads, each head configured to provide a respective expected reward value for a setting of parameters by the policy network. The radar device is further configured to detect that a scene captured by the radar device is not reliably processable based on a distribution of the expected reward values generated by the plurality of heads.

Abstract: In accordance with an embodiment, a method includes estimating a people count of one or more persons included in the scene based on a first range-Doppler measurement map and the second range-Doppler measurement map derived from a radar measurement dataset. Estimating the people count includes inputting the first range-Doppler measurement map into a first data processing pipeline of a neural network algorithm, and inputting the second range-Doppler measurement map into a second data processing pipeline of the neural network algorithm. The first data processing pipeline and the second data processing pipeline includes range-Doppler convolutional layers implementing two-dimensional convolutions along the range dimension and the Doppler dimension, and the neural network algorithm includes an output section for processing a combination of a first output of the first data processing pipeline and a second output of the second data processing pipeline in a regression block.