Google Inc.

Leland Stanford Junior University

Jaime Lien

Patrick M Amihood

Ivan Poupyrev

Nicholas Edward Gillian

Erik M Olson

Changzhan Gu

Abhijit Aroon Shah

Octavio Ponce Madrigal

Carsten C Schwesig

Eiji Hayashi

David J Weber

Hakim Kader Bhai Raja

Vignesh Sachidanandam

Leonardo Giusti

Jiang Zhu

Jian Wang

Houshang Aghahassan

Jian Jim Wang

Andrew C Felch

Cody Blair Wortham

Arnold Robert Feldman

Maryam Tabesh

Luzhou Xu

Michal Matuszak

Blake Charles Jacquot

Jin Yamanaka

This document describes techniques for radio frequency (RF) based micro-motion tracking. These techniques enable even millimeter-scale hand motions to be tracked. To do so, radar signals are used from radar systems that, with conventional techniques, would only permit resolutions of a centimeter or more.

RF-based micro-motion tracking for gesture tracking and recognition

Techniques and apparatuses are described that facilitate ambient computing using a radar system. Compared to other smart devices that rely on a physical user interface, a smart device with a radar system can support ambient computing by providing an eye-free interaction and less cognitively demanding gesture-based user interface. The radar system can be designed to address a variety of challenges associated with ambient computing, including power consumption, environmental variations, background noise, size, and user privacy. The radar system uses an ambient-computing machine-learned module to quickly recognize gestures performed by a user up to at least two meters away. The ambient-computing machine-learned module is trained to filter background noise and have a sufficiently low false positive rate to enhance the user experience.

Facilitating ambient computing using a radar system

Devices are provided that include radar circuits arranged to send and receive radar signals that can be used to, for example, detect gestures performed in the vicinity of the device. Arrangements of the circuits and associated antennas allow for the device to have no bezel or a minimal bezel.

Human and gesture sensing in a computing device

This document describes apparatuses and techniques for radar-enabled sensor fusion. In some aspects, a radar field is provided and reflection signals that correspond to a target in the radar field are received. The reflection signals are transformed to provide radar data, from which a radar feature indicating a physical characteristic of the target is extracted. Based on the radar features, a sensor is activated to provide supplemental sensor data associated with the physical characteristic. The radar feature is then augmented with the supplemental sensor data to enhance the radar feature, such as by increasing an accuracy or resolution of the radar feature. By so doing, performance of sensor-based applications, which rely on the enhanced radar features, can be improved.

Radar-enabled sensor fusion

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-based radar system capable of detecting user gestures in the presence of saturation. In particular, a radar system employs machine learning to compensate for distortions resulting from saturation. This enables gesture recognition to be performed while the radar system's receiver is saturated. As such, the radar system can forgo integrating an automatic gain control circuit to prevent the receiver from becoming saturated. Furthermore, the radar system can operate with higher gains to increasing sensitivity without adding additional antennas. By using machine learning, the radar system's dynamic range increases, which enables the radar system to detect a variety of different types of gestures having small or large radar cross sections, and performed at various distances from the radar system.

Saturation compensation using a smart-device-based radar system

Techniques and apparatuses are described that implement electromagnetic vector sensors (EMVS) for a smart-device-based radar system. Instead of including an antenna array of similar antenna elements, the radar system includes two or more electromagnetic vector sensors. At least one of the electromagnetic vector sensors is used for transmission and at least another of the electromagnetic vector sensors is used for reception. Each electromagnetic vector sensor includes a group of antennas with different antenna patterns, orientations, and/or polarizations. An overall footprint of the two electromagnetic vector sensors (e.g., one for transmission and one for reception) can be smaller than antenna arrays used by other radar systems, thereby enabling the radar system to be implemented within space-constrained devices.

Electromagnetic vector sensors for a smart-device-based radar system

This document describes techniques and systems that enable a mobile device-based radar system for applying different power modes to a multi-mode interface. The techniques and systems include a user device having a radar system, and an interaction manager. The radar system generates a radar field, provides radar data, and operates at one of various different radar-power states. The user device analyzes the radar data to detect a presence or movement of a user within the radar field. Responsive to the detection, the radar system changes from a first radar-power state to a second radar-power state. Based on this change, the interaction manager selects a power mode, for a multi-mode interface, that corresponds to the second radar-power state, and applies the selected power mode to the multi-mode interface to provide a corresponding display via a display device.

Mobile device-based radar system for applying different power modes to a multi-mode interface

Various embodiments wirelessly detect micro gestures using multiple antenna of a gesture sensor device. At times, the gesture sensor device transmits multiple outgoing radio frequency (RF) signals, each outgoing RF signal transmitted via a respective antenna of the gesture sensor device. The outgoing RF signals are configured to help capture information that can be used to identify micro-gestures performed by a hand. The gesture sensor device captures incoming RF signals generated by the outgoing RF signals reflecting off of the hand, and then analyzes the incoming RF signals to identify the micro-gesture.

Gesture recognition using multiple antenna

Techniques and apparatuses are described for radar angular ambiguity resolution. These techniques enable a target's angular position to be determined from a spatial response that has multiple amplitude peaks. Instead of solely considering which peak has a highest amplitude, the techniques for radar angular ambiguity resolution select a frequency sub-spectrum, or multiple frequency sub-spectrums, that emphasize amplitude or phase differences in the spatial response and analyze an irregular shape of the spatial response across a wide field of view to determine the target's angular position. In this way, each angular position of the target has a unique signature, which the radar system can determine and use to resolve the angular ambiguities. Using these techniques, the radar can have an antenna array element spacing that is greater than half a center wavelength of a reflected radar signal that is used to detect the target.

Radar angular ambiguity resolution

Techniques and apparatuses are described that enable full-duplex operation for radar sensing using a wireless communication chipset. A controller initializes or controls connections between one or more transceivers and antennas in the wireless communication chipset. This enables the wireless communication chipset to be used as a continuous-wave radar or a pulse-Doppler radar. By utilizing these techniques, the wireless communication chipset can be re-purposed or used for wireless communication or radar sensing.

Full-duplex operation for radar sensing using a wireless communication chipset

Techniques and apparatuses are described that implement a smart-device-based radar system capable of detecting user gestures in the presence of saturation. In particular, a radar systememploys machine learning to compensate for distortions resulting from saturation. This enables gesture recognition to be performed while the radar system's receiveris saturated. As such, the radar systemcan forgo integrating an automatic gain control circuit to prevent the receiverfrom becoming saturated. Furthermore, the radar systemcan operate with higher gains to increasing sensitivity without adding additional antennas. By using machine learning, the radar system's dynamic range increases, which enables the radar systemto detect a variety of different types of gestures having small or large radar cross sections, and performed at various distances from the radar system

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Mountain View, CA, United States of America

Jaime Lien