scholarly journals Synthesis of a Sparse Planar Phased Array Antenna with Reduced Side-Lobe Level and Beam-Width using Particle Swarm Optimization

2021 ◽  
Vol 15 ◽  
pp. 1387-1393
Author(s):  
Sivaranjan Goswami ◽  
Kumaresh Sarmah ◽  
Kandarpa Kumar Sarma ◽  
Nikos E. Mastorakis
Keyword(s):  
Particle Swarm Optimization ◽  
Wireless Communication ◽  
Particle Swarm ◽  
Beam Width ◽  
Side Lobe Level ◽  
Ann Model ◽  
Binary Matrix ◽  
Rectangular Array ◽  
Swarm Optimization ◽  
Sparse Array

Computer aided synthesis of sparse array is a popular area of research worldwide for the application in radar and wireless communication. The trend is observing new heights with the launch of 5G millimeter wave wireless communication. A sparse array has a fewer number of elements than a conventional antenna array. In this work, a sparse array is synthesized from a 16×16 uniform rectangular array (URA). The synthesis includes an artificial neural network (ANN) model for estimation of the excitation weights of the URA for a given scan-angle. The weights of the sparse array are computed by the Hadamard product of the weight matrix of the URA with a binary matrix that is obtained using particle swarm optimization (PSO). The objective function of the optimization problem is formulated to ensure that the PSLL is minimized for multiple scan-angles. It is shown from experimental analysis that apart from minimizing the PSLL, the proposed approach yields a narrower beam-width than the original URA

scholarly journals Synthesis of a Sparse 2D-Scanning Array using Particle Swarm Optimization for Side-Lobe Reduction

2021 ◽  
Vol 20 ◽  
pp. 112-116
Author(s):  
Sivaranjan Goswami ◽  
Kandarpa Kumar Sarma ◽  
Kumaresh Sarmah
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Side Lobe ◽  
Ann Model ◽  
Phased Array Antenna ◽  
Binary Matrix ◽  
Rectangular Array ◽  
Swarm Optimization ◽  
Wide Range ◽  
Artificial Neural Network Ann

Synthesis of sparse arrays is a promising area of research for a wide range of applications including radar and millimeter-wave wireless communication. The design goal of array thinning problems is to reduce the number of elements of an array without significantly affecting its performance. This work presents a technique for synthesizing a sparse phased-array antenna from a 16×16 uniform rectangular array (URA). The proposed approach reduces the number of elements by 50% without any significant increase in the peak sidelobe level (PSLL) for all possible scan angles in the azimuthal and elevation plans within a finite range of scan angles. The synthesis includes an artificial neural network (ANN) model for estimation of the excitation weights of the URA for a given scan-angle. The weights of the sparse array are computed by the Hadamard product of the weight matrix of the URA with a binary matrix that is obtained using particle swarm optimization (PSO) to minimize the PSLL.

scholarly journals Multiobjective Synthesis of Linear Arrays by Using an Improved Genetic Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Bo Yang
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Weight Vector ◽  
Side Lobe Level ◽  
Improved Genetic Algorithm ◽  
Pattern Synthesis ◽  
Swarm Optimization ◽  
Dynamic Weight

In this paper, an improved genetic algorithm with dynamic weight vector (IGA-DWV) is proposed for the pattern synthesis of a linear array. To maintain the diversity of the selected solution in each generation, the objective function space is divided by the dynamic weight vector, which is uniformly distributed on the Pareto front (PF). The individuals closer to the dynamic weight vector can be chosen to the new population. Binary- and real-coded genetic algorithms (GAs) with a mapping method are implemented for different optimization problems. To reduce the computation complexity, the repeat calculation of the fitness function in each generation is replaced by a precomputed discrete cosine transform matrix. By transforming the array pattern synthesis into a multiobjective optimization problem, the conflict among the side lobe level (SLL), directivity, and nulls can be efficiently addressed. The proposed method is compared with real number particle swarm optimization (RNPSO) and quantized particle swarm optimization (QPSO) as applied in the pattern synthesis of a linear thinned array and a digital phased array. The numerical examples show that IGA-DWV can achieve a high performance with a lower SLL and more accurate nulls.

scholarly journals APLIKASI JARINGAN SARAF TIRUAN DAN PARTICLE SWARM OPTIMIZATION UNTUK PERAMALAN INDEKS HARGA SAHAM BURSA EFEK INDONESIA

2017 ◽  
Vol 6 (1) ◽  
pp. 22
Author(s):  
Desy Wartati ◽  
Nur Aini Masruroh
Keyword(s):  
Particle Swarm Optimization ◽  
Stock Price ◽  
Particle Swarm ◽  
Computation Time ◽  
Bp Algorithm ◽  
Stock Index ◽  
Ann Model ◽  
Training Process ◽  
Backpropagation Algorithm ◽  
Swarm Optimization

Jakarta Composite Index (JCI) is the main stock index in Indonesia Stock Exchange, which indicates the movement of the performance of all stocks listed. The data of stock price index often experience rapid fluctuations in a short time, so it is needed to carry out an analysis to help investor making the right investment decisions. Forecasting JCI is one of the activities that can be done because it helps to predict the value of the stock price in accordance with the past patterns, so it can be a consideration to make a decision. In this research, there are two forecasting models created to predict JCI, which are Artificial Neural Network (ANN) model with (1) Backpropagation algorithm (BP) and (2) Backpropagation algorithm model combined with Particle Swarm Optimization algorithm (PSO). The development of both models is done from the stage of the training process to obtain optimal weights on each network layer, followed by a stage of the testing process to determine whether the models are valid or not based on the tracking signals that are generated. ANN model is used because it is known to have the ability to process data that is nonlinear such as stock price indices and PSO is used to help ANN to gain weight with a fast computing time and tend to provide optimal results. Forecast results generated from both models are compared based on the error of computation time and forecast error. ANN model with BP algorithm generates computation time of training process for 4,9927 seconds with MSE of training and testing process is respectively 0,0031 and 0,0131, and MAPE of forecast results is 2,55%. ANN model with BP algorithm combined with PSO generates computation time of training process for 4,3867 seconds with MSE of training and testing process is respectively 0,0030 and 0,0062, and MAPE of forecast result is 1,88%. Based on these results, it can be concluded that ANN model with BP algorithm combined with PSO provides a more optimal result than ANN model with BP algorithm.

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