Active three-dimensional scene information acquisition method based on dimensionality-reduced sparse representation

Patent metadata

Field	Value
Publication number	US-11474222-B2
Application number	US-201716630847-A
Country	US
Kind code	B2
Filing date	Jul 13, 2017
Priority date	Jul 13, 2017
Publication date	Oct 18, 2022
Grant date	Oct 18, 2022

Abstract

An active three-dimensional scene information acquisition method based on dimensionality-reduced sparse representation is provided. The method jointly processes multiple one-dimensional active detection signals collected synchronously to achieve three-dimensional positioning of objects in a detected scene or three-dimensional reconstruction of a scene structure. Through an active detection system equipped with one transmitter and multiple receivers, simultaneous three-dimensional positioning of multiple targets in a scene or three-dimensional reconstruction of the geometry of the scene is achieved.

First claim

What is claimed is:

1. An active three-dimensional scene information acquisition method based on dimensionality-reduced sparse representation, comprising the following steps:
S 1 : calibrating an active detection system, the active detection system comprising a transmitter and a plurality of one-dimensional detection signal receivers;
S 2 : detecting a detected scene, and collecting a multi-channel detection signal within a detection period; and
S 3 : establishing and solving a sparse representation optimization model according to a calibration result and the collected detection signal.

2. The active three-dimensional scene information acquisition method based on dimensionality-reduced sparse representation according to claim 1 , wherein a specific process of step S 1 is:
making the transmitter and the receivers of the active detection system face towards an open area in which there is only one object, starting the transmitter to transmit a detection signal s, recording a waveform of a single reflected signal received by each receiver, the received waveforms being recorded as s 1 , s 2 , . . . , s n , where n is a number of the receivers, and completing the calibration for the active detection system after completely recording the received waveforms.

3. The active three-dimensional scene information acquisition method based on dimensionality-reduced sparse representation according to claim 2 , wherein a specific process of step S 2 is:
placing a plurality of receivers at a position that is not coplanar with the transmitter, a distance from the transmitter being not less than L, and for a detection signal with a wave velocity of v and a duration of t, L≥vt;
making the transmitter and the receivers face the detected scene, when target information in the scene is to be acquired, activating step S 21 , and when a three-dimensional structure of the scene is to be acquired, activating step S 22 ;
S 21 : transmitting a detection signal s to a scene without a target, recording signals b 1 , b 2 , . . . , b n received by each receiver, when a target appears in a scene, transmitting a detection signal s to the scene with the target, and recording signals z 1 , z 2 , . . . , z n received by each receiver, y 1 =z 1 −b 1 , . . . , y 2 =z 2 −b 2 , . . . , y n =z n −b n ; and
S 22 : transmitting a detection signal s to a scene, and recording signals y 1 , y 2 , . . . , y n , received by each receiver.

4. The active three-dimensional scene information acquisition method based on dimensionality-reduced sparse representation according to claim 3 , wherein a specific process of step S 3 is:
S 31 : dividing the detected scene into a plurality of voxels, and establishing sparse representation dictionaries D 1 , D 2 , . . . , D n ,wherein a j th column of elements of the j th dictionary D i is a waveform that should be received by the j th receiver when the j th voxel has an object, and may be approximated by s i translating to a corresponding position;
S 32 : constructing an overall sparse representation dictionary

D
=

[

D
1

D
2

…

D
n

]

and an overall received signal

y
=

[

y
1

y
2

…

y
n

]

,

the dictionary D being a matrix of p×q, estimating a noise level σ D of the dictionary and a noise level σ s of the received signal, if σ D 2 /∥D∥ F 2 ≤τ and σ s 2 /∥y∥ 2 2 ≤τ, activating step S 33 , otherwise, activating step S 34 , a threshold τ being 0.05;
S 33 : establishing and solving an optimization model

min
x

⁢

1
2

⁢



y
-
DWx



2
2

+

λ
⁢


Wx


1

,

where λ=σ s √{square root over (2 log q)}, so as to obtain three-dimensional information Wx of the scene; and
S 34 : assuming m=min (p,q), and taking an appropriate k, so that

∑

i
=
1

k

⁢

ω
i
2

/

∑

i
=
1

m

⁢

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i
2

≈

σ
s
2

/


y


Assignees

• Univ Sun Yat Sen

Inventors

• Chen Chongyu
• Lin Liang

Classifications

• G01S7/539 Primary

  using analysis of echo signal for target characterisation; Target signature; Target cross-section · CPC title

• G01S15/89

  for mapping or imaging · CPC title

• G01S15/42

  Simultaneous measurement of distance and other co-ordinates (indirect measurement G01S15/46) · CPC title

• G01S13/003

  Bistatic radar systems; Multistatic radar systems · CPC title