Process and device for generating at least one synthetic radio frequency image for machine learning

Patent metadata

Field	Value
Publication number	US-2024125920-A1
Application number	US-202218046746-A
Country	US
Kind code	A1
Filing date	Oct 14, 2022
Priority date	Oct 14, 2022
Publication date	Apr 18, 2024
Grant date	—

Abstract

Disclosed are a process (1) and a device (2) for generating at least one synthetic radio frequency, RF, image for machine learning. The process (1) comprises: having (11) a three-dimensional, 3D, body model of a human; sampling (12) the 3D body model; and generating (13) the at least one synthetic RF image in accordance with an imaging transformation of the 3D body sample. This provides labeled data in the form of RF images for training of machine learning algorithms.

First claim

1 . A process for generating at least one synthetic radio frequency, RF, image for machine learning, the process comprising
having a three-dimensional, 3D, body model of a human; sampling the 3D body model; and generating the at least one synthetic RF image in accordance with an imaging transformation of the 3D body sample.

2 . The process of claim 1 ,
the at least one synthetic RF image comprising a microwave image.

3 . The process of claim 1 ,
the at least one synthetic RF image comprising a plurality of synthetic RF images.

4 . The process of claim 3 ,
the plurality of synthetic RF images representing different shooting angles.

5 . The process of claim 3 ,
the plurality of synthetic RF images representing different body constitutions.

6 . The process of claim 3 ,
the plurality of synthetic RF images representing different body postures.

7 . The process of claim 3 ,
the plurality of synthetic RF images representing different body movements.

8 . The process of claim 1 , wherein having the 3D body model comprises
forming a 3D body model from a computer-aided design, CAD, based body skeleton model and a CAD-based body surface mesh model; shaping the 3D body model in accordance with given shape parameters; and mobilizing the 3D body model in accordance with given motion capture data.

9 . The process of claim 8 , wherein mobilizing the 3D body model comprises
retargeting the given motion capture data onto the body skeleton model of the 3D body model; and skinning the body surface mesh model of the retargeted 3D body model in accordance with a Linear Blend Skinning, LBS, algorithm and given mobilization weights.

10 . The process of claim 1 ,
the imaging transformation comprising one of:
a physical optics based electromagnetic, EM, simulation, and
an inference by a machine learning, ML, algorithm being trained for RF imaging of 3D body samples.

11 . The process of claim 10 ,
the physical optics based simulation comprising ray-based shadowing.

12 . The process of claim 10 ,
the physical optics based simulation taking into account multiple transmitters and receivers.

13 . The process of claim 10 ,
the physical optics based simulation using graphics processing unit, GPU, acceleration.

14 . The process of claim 1 , further comprising
training a further ML algorithm in accordance with the at least one synthetic RF image for inference of a similarity with the at least one synthetic RF image.

15 . The process of claim 14 , further comprising
providing an RF image to be analyzed.

16 . The process of claim 15 , wherein providing the RF image to be analyzed comprises
augmenting the shaped 3D body model with a CAD-based 3D model of an object to be detected; mobilizing the augmented 3D body model in accordance with the given motion capture data; sampling the mobilized augmented 3D body model; and generating the RF image to be analyzed in accordance with the EM simulation of the augmented 3D body sample.

17 . The process of claim 15 , wherein providing the RF image to be analyzed comprises
generating the RF image to be analyzed in accordance with an RF image of an object to be detected and one of the at least one synthetic RF images corresponding in terms of the shooting angle, the body constitution and/or the body posture.

18 . The process of claim 15 , further comprising
inferring from the trained ML algorithm the similarity of the provided RF image to be analyzed with the at least one synthetic RF image.

19 . The process of claim 15 , further comprising
inferring from the trained ML algorithm the similarity with the provided RF image to be analyzed with the at least one synthetic RF image for different simulation setups of the physical optics based simulation; and deriving a robustness of the inference against the different simulation setups.

20 . A device for generating at least one synthetic radio frequency, RF, image for machine learning, the device comprising
a processor, being configured to
have a three-dimensional, 3D, body model of a human;
sample the 3D body model; and
generate the at least one synthetic RF image in accordance with an imaging transformation of the 3D body sample.

Assignees

• Rohde & Schwarz

Inventors

• Baur Christoph
• Schnattinger Georg
• Huber Benedikt

Classifications

• G06V20/52

  Surveillance or monitoring of activities, e.g. for recognising suspicious objects (recognising microscopic objects G06V20/69) · CPC title

• G06T17/20

  Finite element generation, e.g. wire-frame surface description, {tesselation} · CPC title

• G01S13/89 Primary

  for mapping or imaging · CPC title

• G06T7/70

  Determining position or orientation of objects or cameras (camera calibration G06T7/80) · CPC title

• G06V10/761

  Proximity, similarity or dissimilarity measures · CPC title