Abstract: In accordance with an embodiment, a method of training of a machine-learning algorithm includes: obtaining a training dataset comprising multiple training feature vectors and associated ground-truth labels, the multiple training feature vectors representing respective radar measurement datasets; determining, for each one of the multiple training feature vectors, a respective weighting factor by employing an explainable artificial-intelligence analysis of the machine-learning algorithm in a current training state; and training the machine-learning algorithm based on loss values that are determined based on a difference between respective classification predictions made by the machine-learning algorithm in the current training state for each one of the multiple training feature vectors and the ground-truth labels, wherein the loss values are weighted using the respective weighting factors associated with each training feature vector.

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.

Abstract: In an embodiment, a method for radar interference mitigation includes: transmitting a first plurality of radar signals having a first set of radar signal parameter values; receiving a first plurality of reflected radar signals; generating a radar image based on the first plurality of reflected radar signals; using a continuous reward function to generate a reward value based on the radar image; using a neural network to generate a second set of radar signal parameter values based on the reward value; and transmitting a second plurality of radar signals having the second set of radar signal parameter values.

Abstract: In an embodiment, a method includes: receiving first radar data from a millimeter-wave radar sensor; receiving a set of hyperparameters with a radar processing chain; generating a first radar processing output using the radar processing chain based on the first radar data and the set of hyperparameters; updating the set of hyperparameters based on the first radar processing output using a hyperparameter selection neural network; receiving second radar data from the millimeter-wave radar sensor; and generating a second radar processing output using the radar processing chain based on the second radar data and the updated set of hyperparameters.

Abstract: In an embodiment, a method to evaluate radar images includes providing a first raw radar image and a second raw radar image and determining, whether a reliability criterion is fulfilled. The method further includes using a first coordinate and a second coordinate output by a trained neural network as an estimate of a position of an object if the reliability criterion is fulfilled, the trained neural network using the first raw radar image and the second raw radar image as an input. The method further includes using a third coordinate and a fourth coordinate output by another radar processing pipeline as the estimate of the position of the object if the reliability criterion is not fulfilled, the radar processing pipeline using the first raw radar image and the second raw radar image as an input.

Abstract: In an embodiment, a method for radar interference mitigation includes: transmitting a first plurality of radar signals having a first set of radar signal parameter values; receiving a first plurality of reflected radar signals; generating a radar image based on the first plurality of reflected radar signals; using a continuous reward function to generate a reward value based on the radar image; using a neural network to generate a second set of radar signal parameter values based on the reward value; and transmitting a second plurality of radar signals having the second set of radar signal parameter values.