Improved IMM algorithm based on support vector regression for UAV tracking

doi:10.23919/JSEE.2022.000075

Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (4): 867-876.doi: 10.23919/JSEE.2022.000075

• ELECTRONICS TECHNOLOGY • Previous Articles Next Articles

Improved IMM algorithm based on support vector regression for UAV tracking

Yuan ZENG^1,²(), Wenbin LU¹(), Bo YU³(), Shifei TAO³(), Haosu ZHOU¹(), Yu CHEN^2,*()

¹ Shanghai Spaceflight Electronic and Communication Equipment Research Institute, Shanghai 201109, China
² Science and Technology on Near-Surface Detection Laboratory, Wuxi 214035, China
³ School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

Received:2020-12-23 Online:2022-08-30 Published:2022-08-30
Contact: Yu CHEN E-mail:iamzengyuan@hotmail.com;dawen_lu@126.com;yb102725@njust.edu.cn;s.tao@njust.edu.cn;zhouhaosu@sina.com;cy0520tool@sohu.com.cn
About author:|ZENG Yuan was born in 1979. She received her B.S. degree in Hunan Normal University, China, in 2001, and M.S. degree in Wuhan Institute of Physics and Mathematics, China, in 2004, and Ph.D. degree in Shanghai Institute of Microsystem and Information Technology (SIMIT) of the Chinese Academy of Sciences in 2009. From 2009 to 2014, she worked as an assistant professor in SIMIT. She is a senior engineer with Shanghai Spaceflight Electronic and Communication Equipment Research Institute. Her main research interests include signal processing and radar technology. E-mail: iamzengyuan@hotmail.com||LU Wenbin was born in 1980. He received his B.S. degree in electronic engineering from Xidian University in 2003 and M.S. degree in electronic circuit and system from Nanjing University of Aeronautics and Astronautics in 2006. He has been with Shanghai Spaceflight Electronic and Communication Equipment Research Institute. His main research interests include radar technology and signal processing. E-mail: dawen_lu@126.com||YU Bo was born in 1996. He received his B.S. degree in electronic information engineering form Tianjin University of Technology, Tianjin, China, in 2018. He is currently pursuing his M.S. degree in electronics and communication engineering with Nanjing University of Science and Technology, Nanjing, China. His current research interests include machine learning and radar data processing. E-mail: yb102725@njust.edu.cn||TAO Shifei was born in 1987. He received his B.S. and Ph.D. degrees from the Department of Communication Engineering, Nanjing University of Science and Technology (NJUST), Nanjing, China, in 2008 and 2014, respectively. Since 2017, he has been with NJUST, and now he is an associate professor in the Department of Communication Engineering, NJUST. From 2015 to 2016, he was a postdoctoral research associate in electronic and computer engineering in Northeastern University, Boston, USA. His current research interests include the electromagnetic theory and antenna technology, and SAR images processing. E-mail: s.tao@njust.edu.cn||ZHOU Haosu was born in 1989. He received his B.S. degree in communication engineering from Nanjing University of Science and Technology in 2012 where he received his Ph.D. degree in information and communication engineering in 2018. Since April 2018, he has been with Shanghai Spaceflight Electronic and Communication Equipment Research Institute, where he is now an engineer. His main research interests include signal processing, digital communications, indoor position system, automatic identification system, and very high frequency data exchange system. E-mail: zhouhaosu@sina.com||CHEN Yu was born in 1980. He received his B.S. degree in electronic engineering in 2003 from Xidian University. He received his M.S. degree in 2006 from National University of Defense Technology and has been working in Science and Technology on Near-Surface Detection Laboratory since September 2015. He is now an associate researcher. His research interests include signal processing and automatic recognition. E-mail: cy0520tool@sohu.com.cn
Supported by:
This work was supported by the Foundation of Key Laboratory of Near-Surface

Abstract

Abstract:

With the development of technology, the relevant performance of unmanned aerial vehicles (UAVs) has been greatly improved, and various highly maneuverable UAVs have been developed, which puts forward higher requirements on target tracking technology. Strong maneuvering refers to relatively instantaneous and dramatic changes in target acceleration or movement patterns, as well as continuous changes in speed, angle, and acceleration. However, the traditional UAV tracking algorithm model has poor adaptability and large amount of calculation. This paper applies support vector regression (SVR) to the interacting multiple model (IMM) algorithm. The simulation results show that the improved algorithm has higher tracking accuracy for highly maneuverable targets than the original algorithm, and can adjust parameters adaptively, making it more adaptable.

Key words: interacting multiple model (IMM) filter, constant acceleration (CA), unmanned aerial vehicle (UAV), support vector regression (SVR)

Yuan ZENG, Wenbin LU, Bo YU, Shifei TAO, Haosu ZHOU, Yu CHEN. Improved IMM algorithm based on support vector regression for UAV tracking[J]. Journal of Systems Engineering and Electronics, 2022, 33(4): 867-876.

Figures/Tables 10

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Improved IMM algorithm based on support vector regression for UAV tracking

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