Low-angle estimation using frequency-agile refined maximum likelihood algorithm based on optimal fusion

doi:10.23919/JSEE.2021.000045

Journal of Systems Engineering and Electronics ›› 2021, Vol. 32 ›› Issue (3): 538-544.doi: 10.23919/JSEE.2021.000045

• ELECTRONICS TECHNOLOGY • Previous Articles Next Articles

Low-angle estimation using frequency-agile refined maximum likelihood algorithm based on optimal fusion

Sheng CHEN(), Yongbo ZHAO*(), Xiaojiao PANG(), Yili HU(), Chenghu CAO()

¹ National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China

Received:2020-09-04 Online:2021-06-18 Published:2021-07-26
Contact: Yongbo ZHAO E-mail:842561535@qq.com;ybzhao@xidian.edu.cn;1964230291@qq.com;huyili66@126.com;764161366@qq.com
About author:|CHEN Sheng was born in 1993. He received his B.S. degree from Xidian University, Xi’an, China, in 2017. Currently he is pursuing his Ph.D. degree with the Department of Electrical Engineering, Xidian University. His research interests include array signal processing and low-angle estimation with digital array radar.E-mail: 842561535@qq.com||ZHAO Yongbo was born in 1972. He received his M.E. and Ph.D. degrees in electrical engineering from Xidian University, Xi’an, China, in 1997 and 2000, respectively. He is currently a professor with the National Laboratory of Radar Signal Processing, Xidian University. His research interests include adaptive signal processing, array signal processing, MIMO radars, and target tracking.E-mail: ybzhao@xidian.edu.cn||PANG Xiaojiao was born in 1993. She received her B.S. degree from Xidian University, Xi’an, China. She is currently pursuing her Ph.D. degree with the Department of Electrical Engineering, Xidian University. Her research interests include space time adaptive processing and compressed scene.E-mail: 1964230291@qq.com||HU Yili was born in 1994. He received his B.S. degree in signal and information processing from Yangtze University, Jingzhou, China, in 2017. He is currently working toward his Ph.D. degree with the National Laboratory of Radar Signal Processing, Xidian University. His research interests include processing of airborne conformal arrays radar, clutter suppression and MIMO radar signal processing.E-mail: huyili66@126.com||CAO Chenghu was born in 1987. He has received his M.E. degree in signal processing from Fuzhou University, Fuzhou, China, in 2015. He is currently working towards his Ph.D. degree with the Department of Electrical Engineering, Xidian University. His research interests include parameter estimation and multi-target detection and tracking.E-mail: 764161366@qq.com
Supported by:
This work was supported by the Fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project) (B18039)

Abstract

Abstract:

Low elevation estimation, which has attracted wide attention due to the presence of specular multipath, is essential for tracking radars. Frequency agility not only has the advantage of strong anti-interference ability, but also can enhance the performance of tracking radars. A frequency-agile refined maximum likelihood (RML) algorithm based on optimal fusion is proposed. The algorithm constructs an optimization problem, which minimizes the mean square error (MSE) of angle estimation. Thereby, the optimal weight at different frequency points is obtained for fusing the angle estimation. Through theoretical analysis and simulation, the frequency-agile RML algorithm based on optimal fusion can improve the accuracy of angle estimation effectively.

Key words: frequency-agile, maximum likelihood, multipath signal, low-angle estimation

Sheng CHEN, Yongbo ZHAO, Xiaojiao PANG, Yili HU, Chenghu CAO. Low-angle estimation using frequency-agile refined maximum likelihood algorithm based on optimal fusion[J]. Journal of Systems Engineering and Electronics, 2021, 32(3): 538-544.

Figures/Tables 7

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References 24

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Algorithm	Computation complexity
EAMM	$O(KLM + K{M^3} + QK{M^2})$
Conventional RML	$O(KLM\log (L) + QK{M^2})$
Proposed	$O(KLM\log (L) + QK{M^2} + {K^2}{M^2})$

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Low-angle estimation using frequency-agile refined maximum likelihood algorithm based on optimal fusion

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