公开(公告)号：US20240045026A1

公开(公告)日：2024-02-08

申请号：US18358975

申请日：2023-07-26

Applicant: ZHEJIANG UNIVERSITY

Inventor： Zhaoyang ZHANG ,  Xin TONG ,  Yihan ZHANG

IPC: G01S7/41 ,  G01S7/00 ,  H04W64/00

CPC classification number: G01S7/414 ,  G01S7/006 ,  H04W64/00

Abstract: Provided is an iterative focused millimeter wave integrated communication and sensing method, which converts an environmental sensing problem into a compressed sensing reconstruction problem, and realizes the initial coarse sensing of the environment based on an approximate message passing algorithm; according to a background determining method, the present disclosure divides and determines a target object, removes the influence of background scatters on a receiving signal, and removes the background scatters repeatedly and iteratively, so as to obtain a more accurate focus sensing result of the target object. Compared with existing environment sensing reconstruction algorithms, the iterative focused millimeter wave environment sensing algorithm of the present disclosure significantly improves the accuracy of environment sensing, solves the problem that a large-scale environment cannot be accurately sensed due to limited system resources, and provides an efficient environment sensing method for the future design of integrated sensing and communication systems.

公开(公告)号：US20240256738A1

公开(公告)日：2024-08-01

申请号：US18628848

申请日：2024-04-08

Applicant: ZHEJIANG UNIVERSITY

Inventor： Zhaoyang ZHANG ,  Yihan ZHANG ,  Xin TONG

IPC: G06F30/20 ,  G01S5/02

CPC classification number: G06F30/20 ,  G01S5/0284

Abstract: Provided is an environmental sensing method based on model evolution, and the method performs a channel estimation task under the current communication system to obtain channel response data, thereby realizing environmental sensing. Firstly, an interaction mechanism between electromagnetic waves and environmental objective is divided into reflection and transmission. Subsequently, a mathematical model of channel response and environmental objective is constructed, and an environmental sensing problem is modeled as a compressed sensing optimization problem. Lastly, the present disclosure initiates from a baseline model and enables iteration and evolution of the model by solving the objective to solve the compressed sensing optimization problem, ultimately achieving environmental sensing. Compared with the existing environmental sensing reconstruction methods, the environmental sensing method based on model evolution herein closely resembles real electromagnetic wave propagation characteristics and requires less prior environmental information.