Genetic Algorithm for Antenna Array Thinning with Minimization of Side Lobe Level

A concentric circular antenna array (CCAA) consists of elements positioned on the periphery of imaginary circles on a plane having a common centre and different radii. The simplest way to feed the elements of such an array is to use uniform excitation. However, with a non-uniform excitation profile, considerable reduction of the side lobe level (SLL) may be achieved at the cost of the added complexity. The difference of SLLs (with respect to the uniform excitation case) becomes even more prominent when the beamwidth of the antenna needs to be kept fixed. In this paper, we formulate the task of designing a non-uniformly excited CCAA as a constrained optimization problem and use genetic algorithm (GA) to solve the same. The goal is to determine an optimum set of weights for antenna elements which provides a radiation pattern with maximum SLL reduction with the constraint of a fixed beamwidth.

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An optimization technique utilizing genetic algorithm for the synthesis of large thinned planar antenna array with low peak side lobe level

2018 3rd International Conference on Microwave and Photonics (ICMAP) ◽

10.1109/icmap.2018.8354566 ◽

2018 ◽

Author(s):

Kundan Kumar Suman ◽

P Ashwin ◽

Allen V Miranda ◽

V S Gangwar ◽

R K Gangwar

Keyword(s):

Genetic Algorithm ◽

Antenna Array ◽

Optimization Technique ◽

Side Lobe ◽

Side Lobe Level ◽

Planar Antenna ◽

Planar Antenna Array

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Investigation on SIW Slot Antenna Array with Beam Scanning Ability

International Journal of Antennas and Propagation ◽

10.1155/2019/8293624 ◽

2019 ◽

Vol 2019 ◽

pp. 1-7 ◽

Cited By ~ 3

Author(s):

Yanfei Li ◽

Yang Li

Keyword(s):

Genetic Algorithm ◽

Antenna Array ◽

Antenna Arrays ◽

Beam Width ◽

Side Lobe ◽

Slot Antenna ◽

Main Beam ◽

Side Lobe Level ◽

Substrate Integrated Waveguide ◽

Beam Scanning

A sparse substrate integrated waveguide (SIW) slot antenna array and its application on phase scanning are studied in this paper. The genetic algorithm is used to optimize the best arrangement for 8-element and 7-element sparse arrays over an aperture of 4.5λ0. Antenna arrays with feeding networks, for steering the main beam pointing to 0° and −15°, are demonstrated with the SIW technology. The comparison between the sparse array and the conventional uniformly spaced array with the same aperture are presented, which suggest that the same beam width can be obtained with the gain decreased by 0.5 or 1 dBi and the number of element reduced by 2 or 3, respectively. The sparse antenna array with beam scanning ability presented in this paper shows that, while the beam scanning in the range of ±15°, the gain fluctuation is less than 0.3 dBi and the side lobe level is lower than −10 dB.

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An Improved Chaos Genetic Algorithm for T-Shaped MIMO Radar Antenna Array Optimization

International Journal of Antennas and Propagation ◽

10.1155/2014/631820 ◽

2014 ◽

Vol 2014 ◽

pp. 1-6 ◽

Cited By ~ 4

Author(s):

Xin Fu ◽

Xianzhong Chen ◽

Qingwen Hou ◽

Zhengpeng Wang ◽

Yixin Yin

Keyword(s):

Genetic Algorithm ◽

Antenna Array ◽

Chaotic Map ◽

Side Lobe ◽

Mimo Radar ◽

Side Lobe Level ◽

Initial Population ◽

Local Optimum ◽

Radar Antenna ◽

Chaos Genetic Algorithm

In view of the fact that the traditional genetic algorithm easily falls into local optimum in the late iterations, an improved chaos genetic algorithm employed chaos theory and genetic algorithm is presented to optimize the low side-lobe for T-shaped MIMO radar antenna array. The novel two-dimension Cat chaotic map has been put forward to produce its initial population, improving the diversity of individuals. The improved Tent map is presented for groups of individuals of a generation with chaos disturbance. Improved chaotic genetic algorithm optimization model is established. The algorithm presented in this paper not only improved the search precision, but also avoids effectively the problem of local convergence and prematurity. For MIMO radar, the improved chaos genetic algorithm proposed in this paper obtains lower side-lobe level through optimizing the exciting current amplitude. Simulation results show that the algorithm is feasible and effective. Its performance is superior to the traditional genetic algorithm.

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