Gm Global Technolgoy Operations, Inc.

Apple Inc.

Yaron Eshet

Igal Bilik

Oded Bialer

A radar system includes antenna elements and receive channels. An adaptive switch couples the receive channels to a subset of the antenna elements as selected antenna elements. The selected antenna elements receive reflected signals from reflection by objects and each of the receive channels outputs the digital signal based on the reflected signal from the coupled selected antenna element. A controller processes the digital signal from each receive channel to estimate a direction of arrival (DOA) to each object and generate candidate configurations of the switch. Assessing the candidate configurations includes performing a multi-step assessment using a decision tree with each candidate configuration as a root and examining accuracy of an output at a last step in the decision tree to select a selected candidate configuration based on the accuracy. The switch is configured according to the selected candidate configuration prior to receiving the reflected signals for a next iteration.

Antenna selection in a radar system based on multiple detected objects and multi-step planning

Deep learning in a radar system includes obtaining unaliased time samples from a first radar system. A method includes under-sampling the un-aliased time samples to obtain aliased time samples of a first configuration, matched filtering the un-aliased time samples to obtain an un-aliased data cube and the aliased time samples to obtain an aliased data cube, and using a first neural network to obtain a de-aliased data cube. A first neural network is trained to obtain a trained first neural network. The under-sampling of the un-aliased time samples is repeated to obtain second aliased time samples of a second configuration. The method includes training a second neural network to obtain a trained second neural network, comparing results to choose a selected neural network corresponding with a selected configuration, and using the selected neural network with a second radar system that has the selected configuration to detect one or more objects.

Deep learning for de-aliasing and configuring a radar system

A system and method to use deep learning for super resolution in a radar system include obtaining first-resolution time samples from reflections based on transmissions by a first-resolution radar system of multiple frequency-modulated signals. The first-resolution radar system includes multiple transmit elements and multiple receive elements. The method also includes reducing resolution of the first-resolution time samples to obtain second-resolution time samples, implementing a matched filter on the first-resolution time samples to obtain a first-resolution data cube and on the second-resolution time samples to obtain a second-resolution data cube, processing the second-resolution data cube with a neural network to obtain a third-resolution data cube, and training the neural network based on a first loss obtained by comparing the first-resolution data cube with the third-resolution data cube. The neural network is used with a second-resolution radar system to detect one or more objects.

Deep learning for super resolution in a radar system

A method for processing data includes receiving a depth map of a scene containing at least a humanoid head, the depth map comprising a matrix of pixels having respective pixel depth values. A digital processor extracts from the depth map a curvature map of the scene. The curvature map includes respective curvature values of at least some of the pixels in the matrix. The curvature values are processed in order to identify a face in the scene.

Curvature-based face detector

A method for processing data includes receiving a depth map of a scene containing at least a part of a body of a humanoid form. The depth map includes a matrix of pixels having respective pixel depth values. A digital processor extracts from the depth map a curvature map of the scene. The curvature map includes respective curvature values and curvature orientations of at least some of the pixels in the matrix. The depth map is segmented using the curvature values and curvature orientations in the curvature map so as to extract three-dimensional (3D) coordinates of one or more limbs of the humanoid form.

Growing community of inventors

Haifa, Israel

Yaron Eshet