| Efficient Training Data Acquisition Technique for Deep Learning Networks in Radar Applications |
Young-Jae Choi 
, Woojin Cho
, Seungeui Lee
|
|
Radar R&D Center, Hanwha Systems Co. Ltd., Yongin, Korea |
Correspondence:
Young-Jae Choi,Email: radar.yjchoi@hanwha.com
|
|
Received: 11 April 2023 • Revised: 16 September 2023 • Accepted: 2 February 2024 |
|
|
| Abstract |
|
In the field of radar, deep learning techniques have shown considerably superior performance over traditional classifiers in detecting and classifying targets. However, acquiring sufficient training data for deep learning applications is often challenging and time consuming. In this study, we propose a technique for acquiring training data efficiently using a combination of synthesized data and measured background data. We utilized graphics processing unit (GPU)-based physical optics methods to obtain the backscattered field of moving targets. We then generated a virtual dataset by mixing the synthesized target signal with the background signal real data. Subsequently, we trained a convolutional neural network using the virtual dataset to identify three different classes—Bird, Drone, and Background—from a range-Doppler map. When tested using the measurement data, the trained model achieved an accuracy of over 90%, demonstrating the effectiveness of the proposed method in acquiring training data for radar-based deep learning applications. |
| Key words:
Deep Learning, Graphics Processing Units, Physical Optics, Radar, Target Detection |
|
PDF Links
Full text via DOI
Download Citation
