公开(公告)号：US20240355105A1

公开(公告)日：2024-10-24

申请号：US18762595

申请日：2024-07-02

Applicant: DONGHAI LABORATORY ,  ZHEJIANG UNIVERSITY

Inventor： Chunyi SONG ,  Fuyuan AI ,  Hussain AMJAD ,  Zecheng LI ,  Yuying SONG ,  Zhiwei XU

IPC: G06V10/80 ,  G01J5/00 ,  G01J5/08 ,  G06T7/20 ,  G06T7/55 ,  G06V10/143 ,  G06V10/774 ,  G06V10/82 ,  G06V20/58

CPC classification number: G06V10/806 ,  G01J5/0859 ,  G06T7/20 ,  G06T7/55 ,  G06V10/143 ,  G06V10/774 ,  G06V10/82 ,  G06V20/58 ,  G01J2005/0077 ,  G06T2207/10028 ,  G06T2207/10048 ,  G06T2207/20016 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30261 ,  G06V2201/07

Abstract: A method for target detection based on a visible camera, an infrared camera, and a LiDAR is provided. The method designates visible light images, infrared images, and LiDAR point clouds, which are synchronously acquired, as inputs, and generates an input pseudo-point cloud using visible light images and infrared images, to realize alignment of multimodal information in a three-dimensional space and fusion feature extraction. Then the method adopts a cascade strategy to output more accurate target detection results step by step. In the present disclosure, different characteristics of multi-sensors are complemented, which improves and extends traditional target detection algorithms, improves the accuracy and robustness of target detection, and realizes multi-category target detection in a road scene.

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.

公开(公告)号：US20250102623A1

公开(公告)日：2025-03-27

申请号：US18976291

申请日：2024-12-10

Applicant: DONGHAI LABORATORY

Inventor： Chunyi SONG ,  Xinhong XIE ,  Zixian MA ,  Haotian CHEN ,  Nayu LI ,  Haohong XU ,  Bing LAN ,  Zhiwei XU

IPC: G01S7/40 ,  G01S13/02 ,  G06N3/0442 ,  G06N3/0464 ,  G06N3/09 ,  H01Q1/28

Abstract: Embodiments of the present disclosure provide a method for phased array calibration based on CNN-LSTM using power measurement, comprising: establishing a phased array calibration signal model, and utilizing a program to conveniently obtain a large amount of data for training a neural network without the need for actual measurements; converting and preprocessing the generated data, and saving as a training dataset in the form of feature data and labels; establishing a CNN-LSTM network, and inputting the training data with labels into the CNN-LSTM network for training until the CNN-LSTM network converges to obtain the final calibration model; measuring the phased array to be measured to obtain feature data, obtaining a calibration result of the phased array by inputting the feature data into the calibration model obtained from the training. The method is designed to solve problems of low calibration accuracy, low measurement efficiency, and high instrumentation requirements of the existing phased array calibration processes, and the proposed calibration method has a very high calibration efficiency, and the number of measurements required is much lower than that of all current power measurement-based calibration methods.

公开(公告)号：US20250125522A1

公开(公告)日：2025-04-17

申请号：US18991311

申请日：2024-12-20

Applicant: ZHEJIANG UNIVERSITY ,  DONGHAI LABORATORY

Inventor： Chunyi SONG ,  Haotian CHEN ,  Nayu LI ,  Zhiwei XU ,  Xinhong XIE ,  Zixian MA ,  Bing LAN

IPC: H01Q3/26 ,  G06T5/20 ,  G06V10/44

Abstract: Disclosed is a method for fast automatic calibration of a phased array based on a residual neural network. A phase setting matrix is set and an amplitude and a phase of a array far-field complex signal are measured with a network analyzer to obtain an amplitude and phase vector of the array far-field complex signal. A real part, an imaginary part, and a magnitude of the far-field measured complex signal value are separated and normalized, and mapped to RGB three-channel image data. Datasets are automatically generated according to a preset amplitude-phase error range by a simulation software, the datasets are proportionally divided into a training set and a test set to be input into the residual neural network for training to obtain a calibration model. Measured data is input into the calibration model for automatic estimation of the amplitude-phase error of the phased array.