Google Inc.

Abhijit Aroon Shah

Patrick M Amihood

Jaime Lien

David J Weber

Hakim Kader Bhai Raja

Houshang Aghahassan

Jian Jim Wang

Nicholas Edward Gillian

Maryam Tabesh

Abel Seleshi Mengistu

Guohua Sun

Emanoil Felician Bors

Michal Matuszak

Camille Ann Lesko

Alexis K Salazar

Lawrence Frederick Heyl

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 enable low-power radar. The described techniques enable a radar system to reduce overall power consumption, thereby facilitating incorporation and utilization of the radar system within power-limited devices. Power consumption is reduced through customization of the transmission or processing of radar signals within the radar system. During transmission, different duty cycles, transmit powers, or framing structures can be utilized to collect appropriate data based on detected activity in an external environment. During processing, different hardware or different radar pipelines can be utilized to appropriately analyze the radar data. Instead of disabling the radar system, the described techniques enable the radar system to continuously monitor a dynamic environment and maintain responsiveness while conserving power.

Low-power radar

Techniques and apparatuses are described that implement a smart device with an integrated radar system. The radar integrated circuit is positioned towards an upper-middle portion of a smart device to facilitate gesture recognition and reduce a false-alarm rate associated with other non-gesture related motions of a user. The radar integrated circuit is also positioned away from Global Navigation Satellite System (GNSS) antennas and a wireless charging receiver coil to reduce interference. The radar system operates in a low-power mode to reduce power consumption and facilitate mobile operation of the smart device. By limiting a footprint and power consumption of the radar system, the smart device can include other desirable features in a space-limited package (e.g., a camera, a fingerprint sensor, a display, and so forth).

Smart device with an integrated radar system

Techniques and apparatuses are described that enable radar attenuation mitigation. To improve radar performance, characteristics of an attenuator and/or properties of a radar signal are determined to reduce attenuation of the radar signal due to the attenuator and enable a radar system to detect a target located on an opposite side of the attenuator. These techniques are beneficial in situations in which the attenuator is unavoidably located between the radar system and a target, either due to integration within other electronic devices or due to an operating environment. These techniques save power and cost by reducing the attenuation without increasing transmit power or changing material properties of the attenuator.

Radar attenuation mitigation

Techniques and apparatuses are described that enable power management using a low-power radar. The described techniques enable a radar system to reduce overall power consumption, thereby facilitating incorporation and utilization of the radar system within power-limited devices. In one aspect, the radar system can replace other power-hungry sensors and provide improved performance in the presence of different environmental conditions, such as low lighting, motion, or overlapping targets. In another aspect, the radar system can cause other components within the electronic device to switch to an off-state based on detected activity in an external environment. By actively switching the components between an on-state or the off-state, the radar system enables the computing device to respond to changes in the external environment without the use of an automatic shut-off timer or a physical touch or verbal command from a user.

Power management using a low-power radar

Techniques and apparatuses are described that implement smartphone-based power-efficient radar processing and memory provisioning for detecting gestures. The described techniques map different situations that occur with a user to different memory states. A radar processing system's memory management module allocates at least one memory pool according to an active memory state. As the radar system detects different situations that occur with the user, the memory management module reallocates the memory pool for the appropriate memory state, which can adjust a power mode of the radar system. In some cases, physically separate memories may be allocated for different memory states, which respectively include one or more sequences that are executed by different processors. The memory management module enables efficient use of available power and available memory for radar processing such that a total amount of power and a total size of memory used may be significantly reduced for detecting gestures.

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Foster City, CA, United States of America

Abhijit Aroon Shah