Multiobjective System-by-Design for mm-Wave Automotive Radar Antennas

被引:5
|
作者
Rosatti, Pietro [1 ]
Salucci, Marco [1 ]
Poli, Lorenzo [1 ]
Massa, Andrea [1 ,2 ,3 ,4 ]
机构
[1] ELEDIA UniTN Univ Trento, ELEDIA Res Ctr, Dept Civil Environm & Mech Engn DICAM, I-38123 Trento, Italy
[2] Univ Elect Sci & Technol China UESTC, ELEDIA Res Ctr ELEDIA UESTC UESTC, Sch Elect Sci & Engn, Chengdu, Peoples R China
[3] CNIT Univ Trento, ELEDIA Res Unit, I-38123 Trento, Italy
[4] ELEDIA TSINGHUA Tsinghua Univ, ELEDIA Res Ctr, Beijing 100084, Peoples R China
基金
美国国家科学基金会;
关键词
Radar antennas; Optimized production technology; Optimization; Automotive engineering; Space exploration; Reliability; Iterative methods; Antenna synthesis; artificial intelligence (AI); automotive radar antennas; evolutionary algorithms (EAs); machine learning (ML); multiobjective optimization problem (MOP); system-by-design (SbD); OPTIMIZATION; EFFICIENT; ARRAY; ALGORITHMS; FRAMEWORK;
D O I
10.1109/TAP.2022.3233432
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The computationally efficient solution of multiobjective optimization problems (MOPs) arising in the design of modern electromagnetic (EM) systems, such as mm-wave automotive radar antennas, is addressed. Toward this end, a novel system-by-design (SbD) method is developed to effectively explore the solution space and to provide the decision-maker with a set of optimal tradeoff solutions minimizing multiple and (generally) contrasting objectives. The proposed MO-SbD method proves a high computational efficiency, with a remarkable time-saving with respect to a competitive state-of-the-art MOP solution strategy, thanks to the "smart" integration of evolutionary-inspired concepts and operators with artificial intelligence (AI) and machine learning (ML) techniques. Representative numerical results are reported to provide the interested users with useful insights and guidelines on the proposed optimization method as well as to assess its effectiveness in designing mm-wave automotive radar antennas.
引用
收藏
页码:2958 / 2973
页数:16
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