An Adaptive Processing of Linear Array for Target Detection Improvement

Pal

TENCON 2014 - 2014 IEEE Region 10 Conference

et al. 2014

Antenna array optimization in electromagnetics has thrown a growing influence on the communication systems. In this paper, a novel meta-heuristic search method based on social emotional optimization algorithm (SEOA) are applied to determine the best optimal current excitation weights and optimal inter-element spacing of optimized hyper beamforming of linear antenna arrays. Hyper beam is derived from sum and difference beam patterns of the array, each raised to the power of a hyper beam exponent parameter. SEOA is a population-based stochastic optimization algorithm where each individual simulates one natural person. All individuals communicate among them through cooperation and competition to increase the social status. The winner with the highest status is the final solution. As compared to uniformly excited linear antenna array with inter-element spacing of / 2 λ , conventional non-optimized hyper beamforming and optimal hyper beamforming of the same obtained by FFA [18], SEOA applied to the hyper beam of the same array can achieve much greater reduction in SLL and same or less first null beam width (FNBW), keeping the same value of hyper beam exponent parameter. The whole experiment has been performed for 10-, 14-, and 20-element linear antenna arrays.

Section: Design Equationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Social emotional optimization algorithm for beamforming of linear antenna arrays

Pal

TENCON 2014 - 2014 IEEE Region 10 Conference

et al. 2014

“…Consider a broadside linear array of N equally spaced isotropic elements as shown in Fig.3. The array is symmetric in both geometry and excitation with respect to the array center [8].…”

Section: Design Equationsmentioning

confidence: 99%

Seeker Optimization Algorithm for Beamforming of Linear Antenna Arrays

2014 International Conference on Computational Intelligence and Communication Networks

Kar

et al. 2014

This paper presents Seeker Optimization Algorithm (SOA) to the optimization of current excitation weights and uniform inter-element spacing for the optimal design of hyper beamforming of linear antenna arrays. Hyper beamforming is based on sum and difference beam patterns, each raised to the power of hyper beam exponent parameter for linear antenna arrays. In the SOA, the act of human searching capability and understanding are exploited for the purpose of optimization of the hyper beam pattern. In this algorithm, the search direction is based on empirical gradient by evaluating the response to the position changes and the step length is based on uncertainty reasoning by using a simple fuzzy rule. The simulation experiment is performed on 10-, 14-, and 20-element linear antenna arrays with an objective of obtaining maximum Side Lobe Level (SLL) reduction and much more improved first null beam width (FNBW) for SOA. Finally, the SOA based optimal hyper beamforming designs have proven to be superior in achieving the greatest reduction in SLL and much more improved FNBW, keeping the same value of hyper beam exponent.

“…The equations for the creation of sum, difference, and simple hyper beam pattern in terms of two half beams are as follows [8]:…”

Section: Design Equationsmentioning

confidence: 99%

“…Switched beamforming system [6,7] is a system that can choose one pattern from many predefined patterns in order to enhance the received signals. Fixed beamforming uses fixed set of weights and time delays (or phasing) to combine the signals received from the sensors in the array, primarily using only information about the locations of the sensors in space and the wave direction of interest [8]. Adaptive beamforming or phased array is based on the desired signal maximization mode and interference signal minimization mode [9À11].…”

Section: Introductionmentioning

confidence: 99%

CRPSOWM for Linear Antenna Arrays with Improved SLL and Directivity

IETE Journal of Research

Kar

et al. 2015

In this paper, swarm-based algorithms craziness particle swarm optimization with wavelet mutation (CRPSOWM) have been applied for the optimal design of hyper beamforming of linear antenna array. The conventional gradient based optimization techniques are not efficient and get trapped on local optima for such multimodal highly non-linear constrained hyper beamforming optimization problem. Thus, the above evolutionary technique has been adopted. CRPSOWM incorporates a new definition of swarm updating with the help of wavelet theory. Wavelet mutation enhances the CRPSO to explore the solution space more effectively compared to the other optimization methods as reported in firefly algorithm (FFA). Thus CRPSOWM is apparently free from getting trapped at local optima and premature convergence. The simulation results show CRPSOWM outperforms FFA by achieving much greater reduction in side lobe level, improved directivity and much more improved first null beam width keeping the same value of hyper beam exponent. The optimized hyper beam is achieved by optimization of current excitation weights and uniform inter-element spacing. The approach is illustrated through 10-, 14-, and 20-element linear antenna arrays.