Google Inc.

Michal Matuszak

Nicholas Edward Gillian

Ivan Poupyrev

Patrick M Amihood

Jaime Lien

Abhijit Aroon Shah

Abel Seleshi Mengistu

Octavio Ponce Madrigal

Techniques and apparatuses are described that implement a smart-device-based radar system capable of performing gesture recognition using a space time neural network. The space time neural network employs machine learning to recognize a user's gesture based on complex radar data. The space time neural network is implemented using a multi-stage machine-learning architecture, which enables the radar system to conserve power and recognize the user's gesture in real time (e.g., as the gesture is performed). The space time neural network is also adaptable and can be expanded to recognize multiple types of gestures, such as a swipe gesture and a reach gesture, without significantly increasing size, computational requirements, or latency.

Smart-device-based radar system performing gesture recognition using a space time neural network

Techniques and apparatuses are described that implement a smart-device-based radar system capable of performing angular estimation using machine learning. In particular, a radar systemincludes an angle-estimation modulethat employs machine learning to estimate an angular position of one or more objects (e.g., users). By analyzing an irregular shape of the radar system's spatial response across a wide field of view, the angle-estimation modulecan resolve angular ambiguities that may be present based on the angle to the object or based on a design of the radar systemto correctly identify the angular position of the object. Using machine-learning techniques, the radar systemcan achieve a high probability of detection and a low false-alarm rate for a variety of different antenna element spacings and frequencies.

Smart-device-based radar system performing angular estimation using machine learning

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Sunnyvale, CA, United States of America

Michal Matuszak