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The purpose of this paper is to propose radiating system by avoiding electromagnetic interference in unwanted directions and to radiate the energy in the required direction with an optimization technique.

Practically, multiple, incompatible variables require concurrent boost on a synthesis of systematic antenna assemblage. The authors have worked out the main statistic penalty function to ensure all the restrictions. Here, MBPSO (Modified Binary Particle Swarm Optimization) is developed and introduced thin planar synthesis restriction. The sigmoid function is used to update the particle position. Different analytical demonstrations have been carried out, and the exhibited methods are predominant than the algorithms.

A 20 × 10 planar antenna array is synthesized using modified BPSO. The authors have suppressed the PSLL in two principal planes and as well as in the entire f plane. Numerical results state that MBPSO outperforms the other binary BPSO, BCSO, ACO, RGA, GA optimization techniques. MBPSO achieved a −51.84 dB PSLL level, whereas BPSO achieved −48.57 dB with the same 50% thinning.

Planar array antenna formation is one of the most complex syntheses because the array gets filled with more antenna elements. The machine-like complication and implementation of such an antenna arrangement with a broad opening would be expensive. It is not easy to control the required radiation patterns shape by using a uniform feeding network. To get better flexibility for sustaining the sidelobe levelheaded along with consistent amplitude distribution. So as far as prominence has been given to the evolutionary algorithm, find an ideal solution for objective array combinational problems.

The aim of this paper is to explore the potential of particle swarm optimization (PSO) methods for minimizing the sidelobe levels (SLL) and placing null at arbitrary angles of a nonlinear antenna array.

An improved PSO algorithm is designed.

The improved PSO method is an efficient and robust global optimizer for minimizing the SLL and placing null at arbitrary angles of a nonlinear antenna array.

Some improvements, such as the design of some new formulae for both position and velocity updating, the introduction of an age variable, and the devise of an intensification searches using the cross entropy method, are proposed.

The purpose of this paper is to present a deep-learning-based beamforming method for phased array weather radars, especially whose antenna arrays are equipped with large number of elements, for fast and accurate detection of weather observations.

The beamforming weights are computed by a convolutional neural network (CNN), which is trained with input–output pairs obtained from the Wiener solution.

To validate the robustness of the CNN-based beamformer, it is compared with the traditional beamforming methods, namely, Fourier (FR) beamforming and Capon beamforming. Moreover, the CNN is compared with a radial basis function neural network (RBFNN) which is a shallow type of neural network. It is shown that the CNN method has an excellent performance in radar signal simulations compared to the other methods. In addition to simulations, the robustness of the CNN beamformer is further validated by using real weather data collected by the phased array radar at Osaka University (PAR@OU) and compared to, besides the FR and RBFNN methods, the minimum mean square error beamforming method. It is shown that the CNN has the ability to rapidly and accurately detect the reflectivity of the PAR@OU with even less clutter level in comparison to the other methods.

Motivated by the inherit advantages of the CNN, this paper proposes the development of a CNN-based approach to the beamforming of PAR using both simulated and real data. In this paper, the CNN is trained on the optimum weights of Wiener solution. In simulations, it is applied on a large 32 × 32 planar phased array antenna. Moreover, it is operated on real data collected by the PAR@OU.

A solution of a discrete minimax problem is derived, using the standard methods of approximation theory. It represents a problem of the deconvolution‐inverse filtering of a three‐element discrete even signal, subject to a constraint on the output signal sidelobe level. Consequently, properties of this solution, in particular the asymptotic behaviour of the parameter, which represents the degradation of signal‐to‐noise ratio due to the filtering are examined.

A Description of the New Secondary Radar System, SECAR, which has been developed jointly by The Marconi Company and Compagnie Francaise Thomson Houston to provide a Comprehensive Data Link between an Air Traffic Controller and Aircraft fitted with a Secondary Radar Transponder. AN entirely new secondary radar system, SECAR, developed jointly by The Marconi Company and Compagnie Francaise Thomson Houston (CFTH), was demonstrated recently under full operating conditions at Marconi's Rivenhall Establishment, near Witham, Essex. SECAR provides a comprehensive data link between an air‐traffic controller and any aircraft fitted with a secondary radar transponder. Vital information concerning position and movement can be extracted automatically from the aircraft by this system, without attention from the aircrew.

The tremendous growth of wireless applications and the demand for high data rate, the spectrum utilization improvement has been the most crucial challenges for wireless communication. Adapting cognitive radio with orthogonal frequency division multiplexing or offset quadrature amplitude modulation (OFDM/OQAM) improves the spectrum and energy efficiencies.

Thus, it overcomes the spectral leakage problem at the transmitter side and leads to less interference from secondary user (SUs) to primary user (PUs) and between the SUs in cognitive radio technology. The benefit of exploiting pulse shape filtering in the OFDM/OQAM is to not only eliminate the requirement of the guard bands but also reduce the out of band energy transmission, which also improves the spectral isolation from the neighboring systems. But the high peak to average power ratio (PAPR) phenomenon is a common issue in the majority of the multicarrier modulation systems and thus OFDM/OQAM is no exception in this case.

Therefore, this paper aims to examine the effect of integrating the Walsh–Hadamard Transform (WHT) on the power spectral density and investigates the problem of PAPR in the WHT/OQAM system.

Thus, it overcomes the spectral leakage problem at the transmitter side and leads to less interference from SUs to PUs and between the SUs in cognitive radio technology. The benefit of exploiting pulse shape filtering in the OFDM/OQAM is to not only eliminate the requirement of the guard bands but also reduce the out of band energy transmission, which also improves the spectral isolation from the neighboring systems. But the high PAPR phenomenon is a common issue in the majority of the multicarrier modulation systems thus OFDM/OQAM is no exception in this case.

Reviews the underlying principles of digital signal processing (DSP) with little recourse to mathematics. Aims to encourage experimentation to obtain a feel for the processes involved. DSP provides a powerful numeric means of extracting useful information from sensor data applied to a system. Discusses the basic concepts of such processing techniques and introduces some useful algorithms.

The purpose of this paper is to present and solve the problem of adaptive beamforming (ABF) for a uniform linear array (ULA) as an optimization problem. ABF mainly concerns with estimation of weights of antenna array so as to direct the major lobe in the direction of desired user and nulls in the direction of interfering signals with reduced side lobe level (SLL).

The potential of gravitational search algorithm is explored to optimize multi-objective fitness function for ABF using MATLAB software.

The performance of the algorithm has been compared by considering different number of interference signals at different power levels. The proposed algorithm presents good convergence rate and accurate steering of main lobe and nulls with reduced SLL compared to the well-known ABF technique, namely, minimum variance distortionless response (MVDR) and previously reported results. The simulation results are presented in tabular form.

The present work is limited to simulation. The researchers are encouraged to solve the problem of ABF using the proposed approach in hardware.

The application of proposed algorithm is to optimize multi-objective function for ABF with reduced SLL in linear antenna arrays.

The purpose of this research is to design and develop a technique for polyphase code design for the radar system.

The proposed fractional harmony search algorithm (FHSA) performs the polyphase code design. The FHSA binds the properties of the harmony search algorithm and the fractional theory. An optimal fitness function based on the coherence and the autocorrelation is derived through the proposed FHSA. The performance metrics such as power, autocorrelation and cross-correlation measure the efficiency of the algorithm.

The performance metrics such as power, autocorrelation and cross-correlation is used to measure the efficiency of the algorithm. The simulation results show that the proposed optimal phase code design with FHSA outperforms the existing models with 1.420859, 4.09E−07, 3.69E−18 and 0.000581 W for the fitness, autocorrelation, cross-correlation and power, respectively.

The proposed FHSA for the design and development of the polyphase code design is developed for the RADAR is done to reduce the effect of the Doppler shift.

Optimization involves changing the input parameters of a process that is experimented with different conditions to obtain the maximum or minimum result. Increasing interest is shown by antenna researchers in finding the optimum solution for designing complex antenna arrays which are possible by optimization techniques.

Design of antenna array is a significant electro-magnetic problem of optimization in the current era. The philosophy of optimization is to find the best solution among several available alternatives. In an antenna array, energy is wasted due to side lobe levels which can be reduced by various optimization techniques. Currently, developing optimization techniques applicable for various types of antenna arrays is focused on by researchers.

In the paper, different optimization algorithms for reducing the side lobe level of the antenna array are presented. Specifically, genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), cuckoo search algorithm (CSA), invasive weed optimization (IWO), whale optimization algorithm (WOA), fruitfly optimization algorithm (FOA), firefly algorithm (FA), cat swarm optimization (CSO), dragonfly algorithm (DA), enhanced firefly algorithm (EFA) and bat flower pollinator (BFP) are the most popular optimization techniques. Various metrics such as gain enhancement, reduction of side lobe, speed of convergence and the directivity of these algorithms are discussed. Faster convergence is provided by the GA which is used for genetic operator randomization. GA provides improved efficiency of computation with the extreme optimal result as well as outperforming other algorithms of optimization in finding the best solution.

The originality of the paper includes a study that reveals the usage of the different antennas and their importance in various applications.