Subarray weighting method for sidelobe suppression of difference pattern based on genetic algorithm

Author(s):  
Hu Hang ◽  
Lei Lili ◽  
Hu Yi ◽  
Wu Qun
Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3066 ◽  
Author(s):  
Shuo Yang ◽  
Lijun Zhang ◽  
Jun Fu ◽  
Zhanqi Zheng ◽  
Xiaobin Zhang ◽  
...  

This paper proposes a method for designing a 77 GHz series-fed patch array antenna. Based on the traditional genetic algorithm, the study explores different array topologies consisting of the same microstrip patches to optimize the design. The main optimization goal is to reduce the maximum sidelobe level (SLL). A 77 GHz series-fed patch array antenna for automotive radar was simulated, fabricated, and measured by employing this method. The antenna length was limited to no longer than 3 cm, and the array only had a single compact series with the radiation patch about 1.54 mm wide. In the genetic algorithm used for optimization, the maximum sidelobe level was set equal to or less than −14 dB. The measurement results show that the gain of the proposed antenna was about 15.6 dBi, E-plane half-power beamwidth was about ±3.8°, maximum sidelobe level was about −14.8 dB, and H-plane half-power beamwidth was about ±30° at 77 GHz. The electromagnetic simulation and the measurement results show that the 77 GHz antenna designed with the proposed method has a better sidelobe suppression by over 4 dB than the traditional one of the same length in this paper.


2011 ◽  
Vol 48-49 ◽  
pp. 314-317
Author(s):  
Di Wu ◽  
Sheng Yao Yang ◽  
J.C. Liu

The performance optimization of cognitive radio is a multi-objective optimization problem. Existing genetic algorithms are difficult to assign the weight of each objective when the linear weighting method is used to simplify the multi-objective optimization problem into a single objective optimization problem. In this paper, we propose a new cognitive decision engine algorithm using multi-objective genetic algorithm with population adaptation. A multicarrier system is used for simulation analysis, and experimental results show that the proposed algorithm is effective and meets the real-time requirement.


Author(s):  
Manh Tien Nguyen ◽  
Georges M. Fadel ◽  
Paolo Guarneri ◽  
Ivan Mata

Affordance based design (ABD) theory has been presented in several papers and has interested several researchers in the field of design. One criticism of ABD is that the number of affordances identified can be very large, and therefore, the approach may not be amenable for automation. This paper presents a computer based implementation of a process to improve design using affordances. The Affordance Structure Matrix (ASM) design tool is used to identify relevant relationships between the design parameters of an artifact and the affordances (positive and negative) identified by the user. This initial work investigates multiple existing solutions. A user assigns values to critical affordances that are listed in an ASM by visualizing the possible solutions. The parameter values that describe the architecture of the artifact are encoded and fed to a computer code in addition to the multiple affordance values. A Genetic Algorithm is then used to find an optimal combination of design parameters based on the multiple criteria, the affordances, generating new and better concepts. The approach is applied to the redesign of a steering wheel. Multiple variants of steering wheels available in the literature are presented to the user. After twenty generations of the genetic algorithm, using an additive weighting method, an optimal solution is found and presented.


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