Environmental sensing method based on model evolution

Patent metadata

Field	Value
Publication number	US-12346640-B2
Application number	US-202418628848-A
Country	US
Kind code	B2
Filing date	Apr 8, 2024
Priority date	Aug 10, 2022
Publication date	Jul 1, 2025
Grant date	Jul 1, 2025

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.

First claim

What is claimed is:

1. An environmental sensing method based on a model evolution algorithm, applied in a scenario where a base station perceives and images an environmental objective, comprising:
step S1, periodically receiving, by a base station, a pilot sequence signal sent by active users in a space, and
performing channel estimation on the pilot sequence signal to obtain channel response data;
step S2, establishing, via an occlusion effect calculation method based on a position relationship, a reflection and transmission channel model under an occlusion effect;
step S3, discretizing an environmental space,
constructing a mathematical model of a channel response and an environmental objective in association with the reflection and transmission channel model established in the step S2, and
transforming an environmental sensing problem into a generalized compressed sensing optimization problem using the mathematical model of the channel response and the environmental objective; and
step S4, evolving a model based on the mathematical model of the channel response and the environmental objective constructed in the step S3 using the channel response data obtained in the step S1 to obtain an evolved model, and
solving the generalized compressed sensing optimization problem using the evolved model and the channel response data obtained in the step S1; and
step S5, generating objective information of the environmental objective according to solution result of the generalized compressed sensing optimization problem,
imaging, by the base station, of the environmental objective according to the objective information to obtain an image of the environmental objective, and
transmitting the image of the environmental objective from the base station to a display device,
wherein the objective information includes position information, movement speed, and attitude information;
wherein the step S2 further comprises:
sub-step S21, calculating an ideal non-line-of-sight path matrix H m u ,m k ,m r ,n NLOS from an m u -th user to an m k -th antenna of the base station through an n-th scatterer, in a frequency band of an m r -th carrier, as follows:

H

m
u

,

m
k

,

m
r

,
n

NLOS

=

α

m
u

,

m
k

,

m
r

,
n

NLOS

⁢

e

-
j

⁢

φ

m
u

,

m
k

,

m
r

,
n

NLOS

where e represents a natural constant, j represents an imaginary unit, α m u ,m k ,m r ,n NLOS represents an amplitude of a channel, and φ m u ,m k ,m r ,n NLOS represents a phase of the channel;

α

m
u

,

m
k

,

m
r

,
n

NLOS

=

λ

m
r

⁢

σ

/

(

(

4
⁢
π

)

3
2

⁢

d

m
u

,
n

⁢

d

m
k

,
n

)

σ

m
u

,

m
k

,

m
r

,
n

NLOS

=

2
⁢

π
⁡
(

d

m
u

,
n

+

d

m
k

,
n

)

/

λ

m
r

Assignees

• Univ Zhejiang

Inventors

• Zhang Zhaoyang
• Zhang Yihan
• Tong Xin

Classifications

• G01S5/0284

  Relative positioning · CPC title

• H04B17/391

  Modelling the propagation channel · CPC title

• G06F30/20 Primary

  Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30) · CPC title

• H04L25/024 Primary

  channel estimation algorithms · CPC title