Infineon Technologies Ag

Thomas Reinhold Stadelmayer

Avik Santra

Souvik Hazra

Stephan Schoenfeldt

Kevin Kaiser

Ivan Pavlov

In an embodiment, a method includes: receiving radar digital data; processing the radar digital data with a plurality of sine filters to generate a respective plurality of range-slow-time data, where each sine filter is associated with a respective range zone of a plurality of range zones; generating a first presence score based on a first range-slow-time data of the plurality of range-slow-time data, where the first range-slow-time data is associated with the first range zone; and when the first presence score is higher than a predetermined threshold, generating a plurality of synthetic antennas based on the first range-slow-time data, performing angle estimation based on the plurality of synthetic antennas to generate first probability values for a plurality of angle zones associated with the first range zone, and updating an occupancy grid map based on the first probability values.

Radar-based segmented presence detection

In an embodiment, a method includes: generating a target displacement signal indicative of a movement of a human target based on raw digital data generated by a millimeter-wave radar sensor; and estimating a vital sign of the human target based on the target displacement signal, where generating the target displacement signal includes: generating target in-phase (I) and quadrature (Q) (I/Q) data associated with the human target based on the raw digital data, classifying the target I/Q data as high quality data or as low quality data using a first neural network, when the target I/Q data is classified as low quality data, discarding the target I/Q data, when the target I/Q data is classified as high quality data, performing ellipse fitting on the target I/Q data to generate compensated I/Q data, and generating the target displacement signal based on the compensated I/Q data.

Radar-based single target vital sensing

In an embodiment, a method includes: obtaining one or more positional time spectrograms of a radar measurement of a scene comprising an object; and based on the one or more positional time spectrograms and based on a feature embedding of a variational auto-encoder neural network, predicting a gesture class of a gesture performed by the object.

Radar-based gesture classification using a variational auto-encoder neural network

In an embodiment, a method includes: transmitting a plurality of radar signals using a millimeter-wave radar sensor towards a target; receiving a plurality of reflected radar signals that correspond to the plurality of transmitted radar signals using the millimeter-wave radar; mixing a replica of the plurality of transmitted radar signals with the plurality of received reflected radar signals to generate an intermediate frequency signal; generating raw digital data based on the intermediate frequency signal using an analog-to-digital converter; processing the raw digital data using a constrained L dimensional convolutional layer of a neural network to generate intermediate digital data, where L is a positive integer greater than or equal to 2, and where the neural network includes a plurality of additional layers; and processing the intermediate digital data using the plurality of additional layers to generate information about the target.

Growing community of inventors

Wenzenbach, Germany

Thomas Reinhold Stadelmayer