公开(公告)号：US20240004032A1

公开(公告)日：2024-01-04

申请号：US18451818

申请日：2023-08-17

Applicant: ZHEJIANG UNIVERSITY ,  DONGHAI LABORATORY

Inventor： Chunyi SONG ,  Zhihui CAO ,  Zhiwei XU ,  Yuying SONG ,  Fuyuan AI ,  Jingxuan WU

IPC: G01S7/41 ,  G01S13/72 ,  G01S13/536 ,  G06N3/084

CPC classification number: G01S7/417 ,  G01S13/726 ,  G01S13/536 ,  G06N3/084

Abstract: The embodiment of the present disclosure provides a deep neural network (DNN)-based multi-target constant false alarm rate (CFAR) detection method. The method includes: obtaining target values to be measured based on radar IF (IF) signals to be detected, the target values to be measured including a measured frequency value and a measured intensity value of the radar IF signals; obtaining peak sequences based on the target values to be measured; generating a target detection result by processing the peak sequences based on a DNN detector, the DNN detector being a machine learning model; generating approximated maximum likelihood estimation (AMLE) of a scale parameter based on an approximated maximum likelihood estimator; generating a false alarm adjustment threshold based on a preset false alarm rate and the AMLE; and generating a constant false alarm detection result by processing the target detection result based on the false alarm adjustment threshold.