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YAO Yu, LI Yanjie, LI Zeqing, et al., “MIMO Radar Transmit-Receive Design for Extended Target Detection against Signal-Dependent Interference,” Chinese Journal of Electronics, in press, doi: 10.23919/cje.2021.00.140, 2023.
Citation: YAO Yu, LI Yanjie, LI Zeqing, et al., “MIMO Radar Transmit-Receive Design for Extended Target Detection against Signal-Dependent Interference,” Chinese Journal of Electronics, in press, doi: 10.23919/cje.2021.00.140, 2023.

MIMO Radar Transmit-Receive Design for Extended Target Detection against Signal-Dependent Interference

doi: 10.23919/cje.2021.00.140
Funds:  This work was supported by the National Natural Science Foundation of China under Grant (61761019, 61862024)
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  • Author Bio:

    Yu YAO was born in Yichun Jiangxi. He received the M.S. and Ph.D. degrees in information and communication engineering from Southeast University, China, in 2010 and 2015, respectively. He is currently an Associate Professor with the School of Information Engineering, East China Jiaotong University, China. From April 2019 to April 2020, he is a visiting scholar with the Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy. His research interests are in optimization theory, with emphasis on MIMO communications and radar signal processing. (Email: yaoyu@ecjtu.edu.cn)

    Yanjie LI was born in Changsha Hunan. She received the B.S. degree from East China Jiaotong University, Nanchang, China, in 2018. She is currently working toward the M.S. degree with the School of Information Engineering, East China Jiaotong University. Her research interests include array signal processing, MIMO radar waveform design, radar target detection, and optimization theory. (Email: liyanjie9674@163.com)

    Zeqing LI was born in Shaoguan Guangdong. He received the B.S. degree from East China Jiaotong University, Nanchang, China, in 2020. She is currently working toward the M.S. degree with the School of Information Engineering, East China Jiaotong University. Her research interests include array signal processing, MIMO radar waveform design, radar target detection, and optimization theory. (Email: jayrli@163.com)

    Lenan WU was born in Quanzhou Fujian. He received his MS degree in electronics commutation system from Nanjing University of Aeronautics and Astronautics, China, in 1987 and Ph.D degree in signal and information processing from Southeast University, China, in 1997, Since 1997, he has been with Southeast University, where he is a Professor, and the Director of Multimedia Technical Research Institute. His research interests include Pattern Recognition, Artificial Intelligent, Cognitive Radio and Radar Signal Processing. (Email: wuln@seu.edu.cn)

    Haitao LIU was born in Yibin Hubei. He received the B.E. degree in vehicle engineering from Jilin University, China, in 2009, and the Ph.D. degree in vehicle engineering from Tsinghua University, China, in 2015. He is currently an Assistant Professor with the School of Vehicle Engineering, East China Jiaotong University, China. From 2018 to 2019, he is a visiting scholar with the Department of electrical and computer engineering, McMaster University, Canada. His research interests include array signal processing, sources localization. (Email: liuht2005@hotmail.com)

  • Received Date: 2021-04-20
  • Accepted Date: 2021-12-03
  • Available Online: 2022-02-11
  • Considering uncertain knowledge of target aspect angle (TAA), this paper copes with the joint optimization of transmit sequences and receive filter array for the detection of extended target in the presence of clutter disturbance. We consider joint transmit-receive design in multiple-input multiple-output (MIMO) structure to optimize the worst Signal to interference plus noise ratio (SINR) at the output of the receive filter array. Through a suitable reformulation, we propose a sequential optimization algorithm which monotonically enhances the worst SINR value. Each iteration of the process, includes a convex and a worst-case optimization problem which can be handled by the generalized Dinkelbachs method with a lower computational burden. In addition, resorting to several mathematical manipulations, the original problem is transformed into an equivalent convex problem, which can also be solved via interior-point techniques. Finally, the usefulness of two optimization techniques is confirmed through experimental simulation, emphasizing the detection capability improvement generated by the proposed approaches.
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