The Chaotic beamforming adaptive algorithm is new adaptive method for antenna array’s radiation pattern synthesis. This adaptive method based on the optimization of the Least Mean Square algorithm using Chaos theory enables fast adaptation of antenna array radiation pattern, reduction of the noisy reference signal’s impact, and the improvement of the tracking capabilities. We performed simulations for linear and circular antenna arrays. We also compared the performances of the used and existing algorithms in terms of the radiation pattern comparison.
Purpose The purpose of this paper is to propose a chaotic optimization method for identifying the parameters of the Jiles–Atherton (J-A) hysteresis model. Design/methodology/approach The J-A model has five parameters which are assigned with physical meaning and whose determination is demanding. To determine these parameters, the fitness function, which represents the difference between the measured and the modeled hysteresis loop, is formed. Optimal parameter values are the values that minimize the fitness function. Findings The parameters of J-A model for three magnetic materials are determined. The model with the optimal parameters is validated using measured data and comparison with particle swarm optimization algorithm, genetic algorithm, pattern search and simulated annealing algorithm. The results show that the proposed method provides better agreement between measured and modeled hysteresis loop than other methods used for comparison. The proposed method is also suitable for simultaneous optimization of multiple hysteresis loops. Originality/value Chaotic optimization method is implemented for the first time for J-A model parameter identification. Numerical comparisons with results obtained with other optimization algorithms demonstrate that this method is a suitable alternative in parameters identification of J-A hysteresis model. Furthermore, this method is easy to implement and set up.
Purpose The purpose of this paper is to use the proposed algorithm for the fast adaptation of the antenna array radiation pattern on the particular scenario of the incoming signals. The fitness function to be minimized includes the precise estimation of signals’ arrival angles, setting the deep nulls in the directions of the interfering signal, the reduction of the main lobe’s width and the reduction of side lobes. Design/methodology/approach Unlike conventional adaptive algorithms, the proposed algorithm allows synthesis of radiation patterns in the case of a larger number of incident desired and interfering signals. The proposed method also reduces the width of the dead zone. Findings In this paper a comparison of the results obtained from the chaotic beamforming algorithm with the results obtained by using the Sequential Quadratic Programming method is presented. Originality/value The chaotic beamforming algorithm is proposed here. It is based on the optimization of the least mean square and on the variable step-size least mean square algorithms, using chaos theory for synthesis of the radiation pattern of the linear antenna array.
A new geometry for uniplanar, ultra-wideband monopole antenna has been proposed for operations in the 1.8–30 GHz band, thanks to its fractal structure in the form of a cardioid. The antenna has extremely small dimensions at 0.21λ × 0.285λ at the lowest frequency of 1.8 GHz. A parametric analysis of the influence of certain antenna dimensions on its characteristics was performed in order to achieve the widest possible impedance bandwidth. This antenna is designed for low-cost FR-4 substrate because it is primarily intended for use in broadband energy harvesting and IoT systems, but it is also suitable for applications in communication systems. Simulation results show that the antenna has a reflection coefficient (S11) below −10 dB in the entire 1.8 GHz to 30 GHz frequency range, which covers all existing cellular bands: 3G, 4G, 5G Wi-Fi, ISM, satellite communication and radar bands. The antenna exhibits gains up to 5 dBi.
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