Samsung Electronics Co., Ltd.

Bitsensing Inc.

Seoul National University

Mando Corporation

Seongwook Lee

Woosuk Kim

Jae-Eun Lee

Seong Hee Jeong

Hyunwoong Cho

JaeEun Lee

Seung Tae Khang

SeongCheol Kim

Seokhyun Kang

Haesueng Lim

Hae-Seung Lim

Heonkyo Shim

Facial identification of subjects using a millimeter wave radar sensor, including: transmitting a radar signal onto faces of subjects using the millimeter wave radar sensor; receiving and accumulating reflected radar data, wherein the reflected radar data is received from channels of multiple receiving antennas; converting the reflected radar data into an image form suitable as an input to a convolutional neural network; and performing the facial identification on the image form using a classifier trained by the convolutional neural network.

Face identification using millimeter-wave radar sensor data

Extracting target information from signals of a radar and measurement environments, including: transmitting frequency-modulated continuous wave transmit signal using the radar; receiving reflected signal reflected from a human face at multiple antenna elements; multiplying the transmit signal with the reflected signal using a mixer to produce a mixed signal; passing the mixed signal through a low pass filter to produce a baseband signal including sinusoidal signals; extracting a frequency of each sinusoidal signal from the baseband signal to produce extracted frequencies; and measuring a distance between the human face and the radar using the extracted frequencies.

DNN-based human face classification

An object velocity detection method includes: obtaining a first reception signal and a second reception signal that are received in different time intervals through a radar sensor; determining a Doppler effect based on the first reception signal and the second reception signal; determining an angle value of an object based on a signal obtained by compensating for the Doppler effect; obtaining a compensated signal by compensating for the angle value in the second reception signal; and determining a velocity of the object based on the compensated signal.

Method and apparatus with object velocity detection in radar system

An object detection method and apparatus are provided, the object detection method includes receiving a radar reception signal reflected by an object using a radar apparatus, generating a data set for one or more reflection points of the object by processing the radar reception signal, and detecting the object corresponding to the reflection points based on the data set using a neural network-based object detection model.

Method and apparatus for detecting object based on transforming voxel to pixel images using radar signal

According to an embodiment of the present disclosure, a spatial interpolation method for a linear phased array antenna relates to a spatial interpolation method for a linear phased array antenna including a plurality of transmission antenna elements and a plurality of reception antenna elements and includes Stepin which the plurality of reception antenna elements receive a reflected wave reflected from a target, Stepin which an incidence angle of the reflected wave incident on the plurality of reception antenna elements is estimated using an angle estimation algorithm, Stepin which a bad-conditioned antenna element is selected from among the plurality of reception antenna elements, and Stepin which a received signal of the bad-conditioned antenna element is compensated for and the incidence angle of the reflected wave incident on the plurality of reception antenna elements is re-estimated using an angle estimation algorithm.

Spatial interpolation method and apparatus for linear phased array antenna

Growing community of inventors

Seoul, South Korea

Seongwook Lee