Short Bio:

Xiaolong Chen (SM’21) (M’12–SM’21) was born in Yantai, Shandong, China, in 1985. He received the bachelor’s and master’s degrees in signal and information processing and the Ph.D. degree in radar signal processing from Naval Aviation University (NAU), Yantai, in 2008, 2010, and 2014, respectively. He is currently an Associate Professor with the Marine Target Detection Research Group in NAU. He has published more than 100 academic articles, 3 books, and holds 42 national invention patents. His research interests include radar signal processing, especially for marine target detection and recognition.
In 2016, Dr. Chen was selected in the Young Talents Program of China Association for Science and Technology (CAST), and received the Excellent Doctor Dissertation of CIE. In 2017, he received the Chinese Patent Award. In 2019, he won the Civil-military Integration Award of China Industry-University-Research Institute Collaboration Association (CIUR). He was selected for the Young Scientist Award both at 2019 URSI Asia-Pacific Radio Science Conference and 2019 International Applied Computational Electromagnetics Society Symposium, China (ACES). He is the senior member of IEEE and CIE and has served as the Committee Member of CIE Youth Commission, and Vice Executive Secretary of Radar and Information System Committee of CIE Young Scientist Club since 2018. He has been in the Editorial Board of Journal of Radars since 2019, Journal of Signal Processing since 2020, and served as an Associate Editor of IEEE Access since 2018. In 2022, he was supported by the National Science Fund for Excellent Young Scholars. He is the reviewer for IEEE TAES, IEEE TSP, IEEE SPL, IEEE TGRS, IEEE GRSL, IEEE JSTARS, IET RSN, IET SP, IET EL, DSP, and many international conferences.

Title: Long-time integration and applications for drone target radar detection

Abstract: With the gradual opening of low-altitude airspace, "low, slow and small" aircraft represented by UAVs have developed rapidly. Ordinary rotary-wing drones are low cost, easy to operate, few restrictions on liftoff. Illegally flying drones pose a huge threat to flight and public safety, and have become a common challenge and threats faced by the world. The detection and identification of "low, slow and small" drones by radar have been an international problem. In recent years, with the development and application of digital array radar, ubiquitous radar, and MIMO radar, new methods have been provided for drone detection. Compared with traditional radar, in addition to measurement information of distance and azimuth, they can also obtain high-resolution Doppler information of the target via long-time integration to achieve high resolution estimation and excellent target detection performance. This talk will introduce the application of the long-time coherent integration technology for drone target detection, and show the experiment with the digital array ubiquitous radar.