Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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 104 employs machine learning to compensate for distortions resulting from saturation. This enables gesture recognition to be performed while the radar system 104's receiver 304 is saturated. As such, the radar system 104 can forgo integrating an automatic gain control circuit to prevent the receiver 304 from becoming saturated. Furthermore, the radar system 104 can operate with higher gains to increasing sensitivity without adding additional antennas. By using machine learning, the radar system 104's dynamic range increases, which enables the radar system 104 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 104.

Abstract: 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 system 102 includes an angle-estimation module 504 that 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 102's spatial response across a wide field of view, the angle-estimation module 504 can resolve angular ambiguities that may be present based on the angle to the object or based on a design of the radar system 102 to correctly identify the angular position of the object. Using machine-learning techniques, the radar system 102 can achieve a high probability of detection and a low false-alarm rate for a variety of different antenna element spacings and frequencies.

Abstract: This document describes techniques and systems that enable a mobile device-based radar system (104) for providing a multi-mode interface (114). A radar field (110) is used to enable a user device (102, 702) to accurately determine a presence or threshold movement of a user near the user device. The user device provides a multi-mode interface having at least first and second modes and providing a black display or a low-luminosity display in the first mode. The user device detects, based on radar data and during the first mode, a presence or threshold movement by the user relative to the user device and responsively changes the multi-mode interface from the first mode to the second mode. Responsive to the change to the second mode, the user device provides visual feedback corresponding to the implicit interaction by adjusting one or more display parameters of the black display or the low-luminosity display.

Abstract: Various embodiments dynamically learn user-customizable input gestures. A user can transition a radar-based gesture detection system into a gesture-learning mode. In turn, the radar-based gesture detection system emits a radar field configured to detect a gesture new to the radar-based gesture detection system. The radar-based gesture detection system receives incoming radio frequency (RF) signals generated by the outgoing RF signal reflecting off the gesture, and analyzes the incoming RF signals to learn one or more identifying characteristics about the gesture. Upon learning the identifying characteristics, the radar-based gesture detection system reconfigures a corresponding input identification system to detect the gesture when the one or more identifying characteristics are next identified, and transitions out of the gesture-learning mode.

Abstract: 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.

Abstract: Various embodiments utilize application-based processing parameters to dynamically configure a radar-based detection system based upon an operating context of an associated device. A first application with execution priority on a device dynamically configures the radar-based detection system to emit a radar field suitable for a first operating context associated with the first application. The first application can also dynamically configure processing parameters of the radar-based detection system, such as digital signal processing parameters and machine-learning parameters. In some cases, a second application assumes execution priority over the first application, and dynamically reconfigures the radar-based detection system to emit a radar field suitable to a second operating context associated with the second application.

Abstract: 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.

Abstract: 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.

Abstract: 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.