Abstract: Disclosed herein are systems and methods for blind adaptive beamforming narrowband signals with an uncalibrated antenna array using machine learning. A machine-learning model in the spatial domain enhances the signal of interest and mitigates interfering signals. The systems and methods described herein do not require a calibrated antenna array RF chain. They are based on a neural network that learns the structure of the signal of interest and separates it from other signals in the time, frequency, and spatial domains, without the need of any additional information.

Abstract: A head-mounted display (HMD) tracks a user's hand positions, orientations, and gestures using an ultrasound sensor coupled to the HMD. The ultrasound sensor emits ultrasound signals that reflect off the hands of the user, even if a hand of the user is obstructed by the other hand. The ultrasound sensor identifies features used to train a machine learning model based on detecting reflected ultrasound signals. For example, one of the features is the time delay between consecutive reflected ultrasound signals detected by the ultrasound sensor. The machine learning model learns to determine poses and gestures of the user's hands. The HMD optionally includes a camera that generates image data of the user's hands. The image data can also be used to train the machine learning model. The HMD may perform a calibration process to avoid detecting other objects and surfaces such as a wall next to the user.

Abstract: A head-mounted display (HMD) tracks a user's hand positions, orientations, and gestures using an ultrasound sensor coupled to the HMD. The ultrasound sensor emits ultrasound signals that reflect off the hands of the user, even if a hand of the user is obstructed by the other hand. The ultrasound sensor identifies features used to train a machine learning model based on detecting reflected ultrasound signals. For example, one of the features is the time delay between consecutive reflected ultrasound signals detected by the ultrasound sensor. The machine learning model learns to determine poses and gestures of the user's hands. The HMD optionally includes a camera that generates image data of the user's hands. The image data can also be used to train the machine learning model. The HMD may perform a calibration process to avoid detecting other objects and surfaces such as a wall next to the user.