Comparison and Analysis of Airborne Radar Detection Distance and Reconnaissance Distance Based on Big Data Background
Authors
HongYan Wang1, *, Xue Hao1, DianWei He2
1Air Force Aviation University, Changchun, Jilin, China
2Unit 75852 of the PLA, Guangzhou, Guangdong, China
*Corresponding author.
Email: wanghongyan3788@163.com
Corresponding Author
HongYan Wang
Available Online 23 December 2022.
- DOI
- 10.2991/978-94-6463-034-3_31How to use a DOI?
- Keywords
- Airborne radar; radar range; reconnaissance range
- Abstract
Airborne radar is the eyes of fighter jets. The sooner a target is detected, the better it will be on the battlefield. With the advent of the era of big data, the author starts from the perspective of affecting the detection of airborne radar and uses data simulation to summarize the influence of radar detection distance. Then, the factor analysis that affects the reconnaissance distance is obtained according to the data simulation; finally, the relationship between the two values is obtained from many data. We must make full use of information technology to solve some complex problems.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - HongYan Wang AU - Xue Hao AU - DianWei He PY - 2022 DA - 2022/12/23 TI - Comparison and Analysis of Airborne Radar Detection Distance and Reconnaissance Distance Based on Big Data Background BT - Proceedings of the 2022 3rd International Conference on Big Data and Informatization Education (ICBDIE 2022) PB - Atlantis Press SP - 305 EP - 313 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-034-3_31 DO - 10.2991/978-94-6463-034-3_31 ID - Wang2022 ER -