Synthesis of pattern reconfigurable sparse arrays via sequential convex optimizations for monopulse radar applications

ABSTRACT

In this paper, a new array synthesis approach is developed for the design of reconfigurable sparse arrays radiating sum and difference patterns. The proposed approach provides a significant reduction in the complexity of the beam forming network, which is fulfilled by reducing the number of antenna elements in the array and sharing some excitation weights for the sum and difference channels. An iterative scheme is used where the prescribed pattern response in the mainlobe is cast as a multi-convex problem at each step that the nonconvex lower bound constraint is relaxed while including a reweighted $l_1$-norm minimization based on the magnitudes of the elements. Thus a better performance of beam pattern (e.g. narrower 3-dB beamwidth, lower maximum sidelobe levels) and a smaller number of elements can be obtained compared with the case of uniformly spaced array. The practical array imperfections are also compensated in the optimization stage by using worst-case performance optimization technique. Numerical tests are presented and discussed to validate the versatility and effectiveness of the proposed approach.

KEYWORDS:

• Monopulse antennas
• reconfigurable sparse arrays
• sum and difference patterns
• mutual coupling
• multi-convex programming

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Zhengdong Qi  http://orcid.org/0000-0002-6697-6925

Additional information

Notes on contributors

Zhengdong Qi

Zhengdong Qi received the B.S. degree in Electronic Information Engineering from Nanjing University of Information Science & Technology, Nanjing, China, in 2009, the M.S. degree in computer science and technology from Nanjing University of Information Science & Technology, Nanjing, China, in 2013, Currently, he is working at No.724 Research Institute of CSIC, Nanjing, China, His main research interests are signal and information processing.

Yechao Bai

Yechao Bai received the B.S. degree in Electronic Information Science and Technology from Nanjing University, Nanjing, China, in 2005, the Ph.D. degree in signal and information processing from Nanjing University, Nanjing, China, in 2010. Currently, he is teaching at the School of Electronic Science and Engineering, Nanjing University, Nanjing, China. His main research interests are computational intelligence, multi-objective optimization, metaheuristics, and engineering optimization.