收藏切换
The Design of Radar Target Recognition Algorithm Evaluation System Based on Deep Learning
收藏切换
PDF
Xiaomeng MA1, 2, Shuwen FENG2, Hao YUAN2, Pengyu ZHANG2, Yongjian SHEN2
Journal of Telemetry, Tracking and Command | 2024, 45(3) : 24 - 34
Less
收藏切换
Journal of Telemetry, Tracking and Command | 2024, 45(3): 24-34
Artificial Intelligence Technology
The Design of Radar Target Recognition Algorithm Evaluation System Based on Deep Learning
Full
Xiaomeng MA1, 2, Shuwen FENG2, Hao YUAN2, Pengyu ZHANG2, Yongjian SHEN2
Affiliations
  • 1.School of Electronic Engineering, Xidian University, Xi'an 710071, China
  • 2.Beijing Research Institute of Telemetry, Beijing 100076, China
Published: 2024-05-15 doi: 10.12347/j.ycyk.20231201002
Outline
收藏切换

In the radar imaging equipment test, the traditional real scene test method is difficult to construct, the scene is limited, and the test risk is high, so it is urgent to solve the problems of insufficient testing and incomplete evaluation of the target recog-nition algorithm. Aiming at the existing problems, this paper designs a test system for the target recognition algorithm, which can provide the processing and labeling of SAR image and inverse SAR image, as well as the automatic operation, environment configu-ration and performance evaluation of target recognition algorithm. Compared with the traditional test method, the system has the ad-vantages of low cost, short test time, strong controllability and extensibility.

Radar  /  Object recognition algorithm  /  Evaluation system
Xiaomeng MA, Shuwen FENG, Hao YUAN, Pengyu ZHANG, Yongjian SHEN. The Design of Radar Target Recognition Algorithm Evaluation System Based on Deep Learning[J]. Journal of Telemetry, Tracking and Command, 2024 , 45 (3) : 24 -34 . DOI: 10.12347/j.ycyk.20231201002
Year 2024 volume 45 Issue 3
PDF
76
35
Cite this Article
BibTeX
Article Info
doi: 10.12347/j.ycyk.20231201002
  • Receive Date:2023-12-01
  • Online Date:2026-03-18
  • Published:2024-05-15
Article Data
Affiliations
History
  • Received:2023-12-01
  • Revised:2023-12-20
Funding
Affiliations
    1.School of Electronic Engineering, Xidian University, Xi'an 710071, China
    2.Beijing Research Institute of Telemetry, Beijing 100076, China
References
Share
https://castjournals.cast.org.cn/joweb/ycyk/EN/10.12347/j.ycyk.20231201002
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT