Infineon Technologies Ag

Technische Universitat Graz

Franz Pernkopf

Paul Meissner

Mate Andras Toth

Michael Stark

Johanna Rock

Wolfgang Roth

Michael Wohlmayr

Elmar Messner

Alexander Fuchs

A radar device may include a radar receiver to receive a radio frequency (RF) radar signal and generate a digital signal based on the RF radar signal. The digital signal may comprise a plurality of signal segments. The radar device may include a neural network comprising a plurality of layers to process the plurality of signal segments. Each layer of the plurality of layers may have one or more neurons. The plurality of layers may process the plurality of signal segments using weighting factors having values selected from a predetermined set of discrete values. At least one neuron in an output layer of the plurality of layers may provide an output value that indicates whether a respective signal segment or a sample, associated with the at least one neuron, is overlaid with an interfering signal.

FMCW radar with interference signal suppression using artificial neural network

A radar device may include a radar transmitter to output a radio frequency (RF) transmission signal including a plurality of frequency-modulated chirps. The radar device may include a radar receiver to receive an RF radar signal, and generate, based on the RF radar signal, a dataset including a set of digital values, the dataset being associated with a chirp or a sequence of successive chirps. The radar device may include a neural network to filter the dataset to reduce an interfering signal included in the dataset, the neural network being a convolutional neural network. At least one layer of the neural network may be a complex-valued neural network layer includes complex-valued weighting factor, where the complex-valued neural network layer is configured to perform one or more operations according to a complex-valued computation.

A method for a radar device is described below. According to an example implementation, the method comprises transmitting an RF transmission signal that comprises a plurality of frequency-modulated chirps, and receiving an RF radar signal and generating a dataset containing in each case a particular number of digital values based on the received RF radar signal. A dataset may in this case be associated with a chirp or a sequence of successive chirps. The method furthermore comprises filtering the dataset by way of a neural network to which the dataset is fed in order to reduce an interfering signal contained therein. A convolutional neural network is used as the neural network.

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Graz, Austria

Franz Pernkopf