scholarly journals Optimized Hyper Beamforming of Linear Antenna Arrays Using Collective Animal Behaviour

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Gopi Ram ◽  
Durbadal Mandal ◽  
Rajib Kar ◽  
Sakti Prasad Ghoshal
Keyword(s):  
Antenna Arrays ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Beam Width ◽  
Animal Behaviour ◽  
Linear Antenna ◽  
Sidelobe Level ◽  
Real Coded Genetic Algorithm ◽  
Linear Antenna Arrays ◽  
Collective Animal Behaviour

A novel optimization technique which is developed on mimicking the collective animal behaviour (CAB) is applied for the optimal design of hyper beamforming of linear antenna arrays. Hyper beamforming is based on sum and difference beam patterns of the array, each raised to the power of a hyperbeam exponent parameter. The optimized hyperbeam is achieved by optimization of current excitation weights and uniform interelement spacing. As compared to conventional hyper beamforming of linear antenna array, real coded genetic algorithm (RGA), particle swarm optimization (PSO), and differential evolution (DE) applied to the hyper beam of the same array can achieve reduction in sidelobe level (SLL) and same or less first null beam width (FNBW), keeping the same value of hyperbeam exponent. Again, further reductions of sidelobe level (SLL) and first null beam width (FNBW) have been achieved by the proposed collective animal behaviour (CAB) algorithm. CAB finds near global optimal solution unlike RGA, PSO, and DE in the present problem. The above comparative optimization is illustrated through 10-, 14-, and 20-element linear antenna arrays to establish the optimization efficacy of CAB.

A Crossover Improved Genetic Algorithm and Its Application in Non-Uniform Linear Antenna Arrays

2017 ◽  
Vol 16 (04) ◽  
pp. 1750027 ◽  
Author(s):  
Bhargav Appasani ◽  
Rahul Pelluri ◽  
Vijay Kumar Verma ◽  
Nisha Gupta
Keyword(s):  
Genetic Algorithm ◽  
Antenna Arrays ◽  
Optimization Technique ◽  
Beam Width ◽  
Side Lobe ◽  
Optimization Techniques ◽  
Linear Antenna ◽  
Improved Genetic Algorithm ◽  
Element Array ◽  
Linear Antenna Arrays

Genetic Algorithm (GA) is a widely used optimization technique with multitudinous applications. Improving the performance of the GA would further augment its functionality. This paper presents a Crossover Improved GA (CIGA) that emulates the motion of fireflies employed in the Firefly Algorithm (FA). By employing this mimicked crossover operation, the overall performance of the GA is greatly enhanced. The CIGA is tested on 14 benchmark functions conjointly with the other existing optimization techniques to establish its superiority. Finally, the CIGA is applied to the practical optimization problem of synthesizing non-uniform linear antenna arrays with low side lobe levels (SLL) and low beam width, both requirements being incompatible. However, the proposed CIGA applied for the synthesis of a 12 element array yields an SLL of [Formula: see text]29.2[Formula: see text]dB and a reduced beam width of 19.1[Formula: see text].

Optimized hyper beamforming of receiving linear antenna arrays using Firefly algorithm

2013 ◽  
Vol 6 (2) ◽  
pp. 181-194 ◽  
Author(s):  
Gopi Ram ◽  
Durbadal Mandal ◽  
Rajib Kar ◽  
Sakti Prasad Ghoshal
Keyword(s):  
Antenna Array ◽  
Antenna Arrays ◽  
Firefly Algorithm ◽  
Beam Width ◽  
Linear Antenna ◽  
Linear Antenna Array ◽  
Heuristic Search Method ◽  
Real Coded Genetic Algorithm ◽  
Beam Patterns ◽  
Linear Antenna Arrays

In this paper, an optimized hyper beamforming method is presented based on a hyper beam exponent parameter for receiving linear antenna arrays using a new meta-heuristic search method based on the Firefly algorithm (FFA). A hyper beam is derived from the sum and difference beam patterns of the array, each raised to the power of a hyper beam exponent parameter. As compared to the conventional hyper beamforming of the linear antenna array, FFA applied to the hyper beam of the same array can achieve much more reduction in sidelobe level (SLL) and improved first null beam width (FNBW), keeping the same value of the hyper beam exponent. As compared to the uniformly excited linear antenna array with inter-element spacing of λ/2, conventional non-optimized hyper beamforming and optimal hyper beamforming of the same obtained by real-coded genetic algorithm, particle swarm optimization and Differential evolution, FFA applied to the hyper beam of the same array can achieve much greater reduction in SLL and same or less FNBW, keeping the same value of the hyper beam exponent parameter. The whole experiment has been performed for 10-, 14-, and 20-element linear antenna arrays.

Collective Animal Behaviour Based Optimization Algorithm for IIR System Identification Problem

2014 ◽  
Vol 5 (1) ◽  
pp. 1-35
Author(s):  
P. Upadhyay ◽  
R. Kar ◽  
D. Mandal ◽  
S. P. Ghoshal
Keyword(s):  
System Identification ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Identification Problem ◽  
Animal Behaviour ◽  
Infinite Impulse Response ◽  
Present System ◽  
Real Coded Genetic Algorithm ◽  
Identification Approach ◽  
Collective Animal Behaviour

In this paper a novel optimization technique which is developed on mimicking the collective animal behaviour (CAB) is applied to the infinite impulse response (IIR) system identification problem. Functionality of CAB is governed by occupying the best position of an animal according to its dominance in the group. Enrichment of CAB with the features of randomness, stochastic and heuristic search nature has made the algorithm a suitable tool for finding the global optimal solution. The proposed CAB has alleviated from the defects of premature convergence and stagnation, shown by real coded genetic algorithm (RGA), particle swarm optimization (PSO) and differential evolution (DE) in the present system identification problem. The simulation results obtained for some well known benchmark examples justify the efficacy of the proposed system identification approach using CAB over RGA, PSO and DE in terms of convergence speed, unknown plant coefficients and mean square error (MSE) values produced for IIR system models of both the same order and reduced order.

Collective-Animal-Behaviour-Based Optimized Null Placement in Time-Modulated Linear Antenna Arrays

2017 ◽  
pp. 115-132
Author(s):  
Gopi Ram ◽  
Rajib Kar ◽  
Durbadal Mandal ◽  
Sakti Prasad Ghoshal
Keyword(s):  
Antenna Arrays ◽  
Excitation Amplitude ◽  
Animal Behaviour ◽  
Optimal Placement ◽  
Linear Antenna ◽  
Rf Switch ◽  
Optimal Current ◽  
Null Placement ◽  
Linear Antenna Arrays ◽  
Collective Animal Behaviour

In this paper optimal design of time modulated linear antenna arrays (TMLAA) with optimal placement of nulls in the desired direction of elevation plane has been dealt with the approach based on evolutionary algorithm like collective animal behaviour (CAB). Analysis has been done in theoretical and practical environment. Firstly the current excitation weights of the linear array of isotropic elements have been optimized by CAB is applied to improve null performance of TMLAA by Radio Frequency (RF) switch in MATLAB environment. The nulls positions of a TMLAA can be reduced significantly by optimizing the static excitation amplitudes and proper design of switch-on time intervals of each element. The CAB adjusts the current excitation amplitude of each element to place deeper nulls in the desired directions. Secondly the obtained optimal current excitation weight of the array factor is practically implemented in computer simulation technology- microwave studio (CST- MWS) environment. The array of microstrip patch antenna has been designed to operate at 5.85 GHz.

scholarly journals Correction of failure in linear antenna arrays with greedy sparseness constrained optimization technique

PLoS ONE ◽  
2017 ◽  
Vol 12 (12) ◽  
pp. e0189240 ◽  
Author(s):  
Shafqat Ullah Khan ◽  
M. K. A. Rahim ◽  
Murtala Aminu-Baba ◽  
N. A. Murad
Keyword(s):  
Constrained Optimization ◽  
Antenna Arrays ◽  
Optimization Technique ◽  
Linear Antenna ◽  
Linear Antenna Arrays

scholarly journals A Hybrid GA/GE Beamforming Technique for Side Lobe Cancellation of Linear Antenna Arrays

2021 ◽  
Vol 10 (2) ◽  
pp. 67-77
Author(s):  
S. I. Abdelrahman ◽  
A. H. Hussein ◽  
A. E. A. Shaalan
Keyword(s):  
Antenna Arrays ◽  
Optimization Technique ◽  
Side Lobe ◽  
Main Beam ◽  
Side Lobe Level ◽  
Linear Antenna ◽  
Gauss Elimination ◽  
Half Power ◽  
Beamforming Technique ◽  
Linear Antenna Arrays

Side lobe level reduction is one of the most critical research topics in antenna arrays beamforming as it mitigates the interfering and jamming signals. In this paper, a hybrid combination between the Genetic algorithm (GA) optimization technique and the gauss elimination (GE) equation solving technique is utilized for the introduction of the proposed GA/GE beamforming technique for linear antenna arrays. The proposed technique estimates the optimum excitation coefficients and the non-uniform inter-elements spacing for a specific side lobe (SL) cancellation without disturbing the half power beamwidth (HPBW) of the main beam. Different size Chebychev linear antenna arrays are taken as simulation targets. The simulation results revealed the effectiveness of the proposed technique

scholarly journals A Feeding Network with Chebyshev Distribution for Designing Low Side-lobe Level Antenna Arrays

2017 ◽  
Author(s):  
Toan The Tang ◽  
Tran Minh Nguyen ◽  
Giang Truong Vu Bang
Keyword(s):  
Antenna Arrays ◽  
Linear Array ◽  
Side Lobe ◽  
Power Divider ◽  
Side Lobe Level ◽  
Linear Antenna ◽  
Sidelobe Level ◽  
Weighting Method ◽  
Low Sidelobe ◽  
Linear Antenna Arrays

This paper proposes a feeding networking to gain low sidelobe levels for microstrip linear antenna arrays. The procedure to design a feeding network using Chebyshev weighting method will be proposed and presented. As a demonstration, a feeding network for 8×1 elements linear array with Chebyshev distribution weights (preset sidelobe level of -25 dB) has been designed. An unequal T-junction power divider has been applied in designing the feeding network to guarantee the output powers the same as Chebyshev weights. The obtained results of the amplitudes at each output port have been validated with theory data. The phases of output signals are almost equal at all ports. The proposed feeding network, therefore, can be a good candidate for constructing a low sidelobe level linear array antenna.

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