公开(公告)号：US20240028962A1

公开(公告)日：2024-01-25

申请号：US18346532

申请日：2023-07-03

Applicant: Infineon Technologies AG

Inventor： Lorenzo Servadei ,  Huawei Sun ,  Avik Santra

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: In accordance with an embodiment, a method of training of a machine-learning algorithm includes: obtaining a training dataset comprising multiple training feature vectors and associated ground-truth labels, the multiple training feature vectors representing respective radar measurement datasets; determining, for each one of the multiple training feature vectors, a respective weighting factor by employing an explainable artificial-intelligence analysis of the machine-learning algorithm in a current training state; and training the machine-learning algorithm based on loss values that are determined based on a difference between respective classification predictions made by the machine-learning algorithm in the current training state for each one of the multiple training feature vectors and the ground-truth labels, wherein the loss values are weighted using the respective weighting factors associated with each training feature vector.

公开(公告)号：US20230393240A1

公开(公告)日：2023-12-07

申请号：US18317749

申请日：2023-05-15

Applicant: Infineon Technologies AG

Inventor： Lorenzo Servadei ,  Souvik Hazra ,  Julius Ott ,  Avik Santra ,  Huawei Sun ,  Michael Stephan

IPC: G01S7/41 ,  G01S13/58

CPC classification number: G01S7/417 ,  G01S13/584 ,  G01S7/415

Abstract: In accordance with an embodiment, a method includes estimating a people count of one or more persons included in the scene based on a first range-Doppler measurement map and the second range-Doppler measurement map derived from a radar measurement dataset. Estimating the people count includes inputting the first range-Doppler measurement map into a first data processing pipeline of a neural network algorithm, and inputting the second range-Doppler measurement map into a second data processing pipeline of the neural network algorithm. The first data processing pipeline and the second data processing pipeline includes range-Doppler convolutional layers implementing two-dimensional convolutions along the range dimension and the Doppler dimension, and the neural network algorithm includes an output section for processing a combination of a first output of the first data processing pipeline and a second output of the second data processing pipeline in a regression block.