Multiple beamforming with null steering based on improved invasive weed optimization

Pattern synthesis of non-uniform elliptical antenna arrays is presented in this paper. Only the element positions of the antenna arrays are optimized by the combination of differential evolution (DE) and invasive weed optimization (IWO) to reduce the peak side lobe level (PSLL) of the radiation pattern. In order to avoid the overlap of the array elements, the minimum spacing of the adjacent elements is constrained. Also, the beam width of the radiation pattern can be constrained effectively. Three elliptical antenna arrays that have 8, 12, and 20 elements are investigated. The synthesis results show that the introduced method can present a good side lobe reduction for the radiation pattern. Compared with other optimization methods, the method proposed in this paper can obtain better performance.

Planar antenna array design is one of the most important electromagnetic optimization problems of current interest. This paper introduces a recently developed metaheuristic algorithm, known as the Invasive Weed Optimization (IWO), to the pattern synthesis of planar antenna arrays with desired pattern nulls and sidelobe level by amplitude-only and position-only optimization. The steps in the problem formulation are presented along with a design example that illustrates the performance of the IWO algorithm. Three examples have been presented and solved. Simulation results are proposed to compare with published ones to verify the efiectiveness of the IWO algorithm for planar arrays.

In this paper, a novel method for pattern synthesis of antenna arrays based on dynamic cooperative gray wolf optimizer algorithm is proposed. By introducing the dynamic cooperative weight factor, positions of the three leader wolves are weighted, and then the positions of next generation are updated. The improved algorithm emphasizes the leading role of the leader wolf, but also does not neglect the cooperation between the leadership wolves. Global optimal and local optimal can be well balanced and converge to the optimal solution quickly and effectively. The proposed algorithm has been successfully used to reduce the sidelobe level and form deep null steerings at designated directions which meets the design requirements.

This paper introduces an application of the dandelion optimization (DO) algorithm in antenna arrays. This is the first time that the DO algorithm has been used for optimizing antenna arrays. For antenna array optimization, sidelobe level (SLL) and deep nulls are key technical indicators. A lower SLL can improve the signal-to-noise ratio and reduce the impact of clutter signals outside the main beam. Deep nulls need to be aligned with the direction of interference to eliminate the influence of interference sources. The combination of the two can effectively improve the anti-interference ability of the entire system. Therefore, antenna arrays with ultra-low sidelobes and ultra-deep nulls are currently hot in the field of antenna array design and are also some of the key technologies needed to achieve modern high-performance radar systems. As a new type of evolutionary algorithm inspired by nature, the DO algorithm is inspired by the wind propagation behavior of dandelions in nature. This algorithm iteratively updates the population from three stages of ascent, descent, and landing, ultimately finding the optimal position. It has good optimization ability in solving complex problems such as those involving nonlinearity, discreteness, and non-convexity, and the antenna array pattern synthesis optimization problem belongs to multivariate nonlinear problems. Therefore, the DO algorithm can be effectively applied in the field of antenna array optimization. In this work, we use the following method to obtain an optimized pattern of a linear array with the lowest sidelobe level (SLL), null placement in particular directions, and a lower notch in particular directions: by controlling the antenna array’s element spacing and leaving the phase unchanged to optimize the current amplitudes and by controlling the excitation current and phase fixation of the antenna array and changing the element spacing. In the first and second examples, different algorithms are used to reduce the SLL of the antenna. In the first example, the DO algorithm reduces the SLL to −33.37 dB, which is 2.67 dB, 2.67 dB, 3.77 dB, 2.74 dB, and 2.52 dB lower than five other algorithms. In the second example, the SLL optimized by the DO algorithm is −42.56 dB, which is 5.04 dB and 1.48 dB lower than two other algorithms. In both examples, the DO algorithm reduces the SLL lower than other algorithms when the main lobe of the antenna is not significantly widened. Examples 3, 4, and 5 use the DO algorithm to optimize the amplitude of the current, generating deep nulls and deep notches in specific directions. In Example 3, the DO algorithm obtains a depth of nulls equal to −187.6 dB, which is 66.7 dB and 44.3 dB lower than that of the flower pollination algorithm (FPA) and the chaotic colony predation algorithm (CCPA), respectively. In Example 4, the deep null obtained by the DO algorithm is as low as −98.69 dB, which is 6.67 dB lower than the deep null obtained by the grey wolf optimization (GWO) algorithm. In Example 5, the deep notch obtained by the DO algorithm is as low as −63.1 dB, which is 6.4 dB and 1.9 dB lower than the spider monkey optimization (SMO) algorithm and the grasshopper optimization algorithm (GOA), respectively. The data prove that the DO algorithm produces deeper nulls and notches than other algorithms. The last two examples involve reducing sidelobe levels and generating deep nulls by optimizing the spacing between elements. In Example 5, the SLL obtained using the DO algorithm is −22.8766 dB, which is 0.1998 dB lower than the lowest SLL of −22.6768 dB among other algorithms. In Example 6, the SLL obtained using the DO algorithm is −20.1012 dB, and the null depth is −125.1 dB, which is 1.592 dB lower than the SLL obtained by the cat swarm optimization (CSO) algorithm and 19.1 dB lower than the deep null obtained by the GWO algorithm, respectively. In summary, the results of six simulation experiments indicate that the DO algorithm has better optimization ability in linear array optimization than other evolutionary algorithms.

The pattern synthesis technique of antenna arrays has a wide range of applications in radar and communication systems, which is an important research direction in the field of smart antennas. In order to improve the optimization performance of pattern synthesis of linear antenna arrays, an optimization method based on the Modified RIME Optimization Algorithm (MRIME) is proposed. RIME is a new heuristic algorithm inspired by the condensation process of frost ice in nature, which has a unique update mechanism with strong convergence as well as randomization. In the present work, MRIME is used for optimal pattern synthesis of a linear antenna array (LAA). One part of the present study is to optimize the side lobe level by optimizing the antenna current amplitude while maintaining a uniform spacing; the other part is to optimize the antenna position while assuming a uniform excitation in the synthesis of a sparse LAA, where constraints are imposed on the spacing of the array elements and aperture length, and suppression of the side lobe level with the position of the zeros in the specified direction is also achieved. In this article, simulation experiments for uniform linear arrays as well as sparse linear arrays are presented in detail, and the results show that the MRIME optimization algorithm outperforms most of the existing evolutionary classes of optimization algorithms in optimizing the side lobe level. This illustrates the potential of utilizing the MRIME optimization algorithm for antenna arrays and various other electromagnetism-related challenges.

본 논문에서는 2차 함수 곡선의 회전체 곡면 위에 있는 배열 안테나의 빔 합성을 위한 Enhanced Adaptive Genetic Algorithm(EAGA)을 제안하였다. 제안된 알고리즘은 더 빠른 수렴 속도와 더 낮은 비용함수 값을 얻기 위해 Adaptive Genetic Algorithm(AGA)과 Invasive Weed Optimization(IWO)을 결합시켰다. 각 안테나 소자의 급전 크기와 위상의 최적화된 값은 EAGA를 통해 구하였으며, 이 결과를 통해 EAGA가 컨포멀 배열 안테나의 패턴 합성 알고리즘으로써 AGA보다 더 우수함을 보였다. This paper proposes an enhanced adaptive genetic algorithm(EAGA) dedicated to pattern synthesis of array antenna which conforms to a curved surface of rotation with quadratic function. EAGA combines adaptive genetic algorithm(AGA) with invasive weed optimization(IWO) for the faster convergence and the lower cost value of the cost function. The amplitude and phase of each excited weighting factor are optimized to meet the required goals using EAGA. The EAGA results indicate that the proposed algorithm is superior to AGA when applied to the problem of conformal array antenna pattern synthesis.

Far-field pattern synthesis of small antenna arrays is a nontrivial problem, mainly due to mutual coupling, often precluding the use of conventional tapering algorithms. A technique to compensate for mutual coupling in small linear antenna arrays is described. The method consist of calculations of a coupling matrix, which is then used to compensate for the effects of mutual coupling. Two different methods of the calculation of coupling coefficients between the array elements are presented. In order to validate the methods, measured data for a small linear antenna array is presented and discussed.

A new hybrid evolutionary algorithm for multi-objective optimal power flow in an integrated WE, PV, and PEV power system

A simple and flexible genetic algorithm (GA) for pattern synthesis of antenna array with arbitrary geometric configuration is presented. Unlike conventional GA using binary coding and binary crossover, this approach directly represents the array excitation weighting vectors as complex number chromosomes and uses decimal linear crossover without a crossover site. Compared with conventional GAs, this approach has a few advantages: giving a clearer and simpler representation of the problem, simplifying chromosome construction, and totally avoiding binary encoding and decoding so as to simplify software programming and to reduce CPU time. This method also allows us to impose constraints on phases and magnitudes of complex excitation coefficients for preferable implementation in practice using digital phase shifters and digital attenuators. Successful applications show that the approach can be used as a general tool for pattern synthesis of arbitrary arrays.

A fast algorithm is given for computing the optimal weight vector in antenna array pattern synthesis having a set of null constraints. The algorithm requires only a UDUT decomposition of an m + 1 × m + 1 real positive definite matrix and the computation of the first row of the inverse of a triangular matrix of the same dimension, where m is the number of null constraints.

In this paper, a novel approach is introduced to estimate the North American Aerospace Defense Command (NORAD)-type two-line elements (TLE) data based on the invasive weed optimization (IWO) method, which is a stochastic numerical optimization algorithm inspired by colonizing weeds. Many satellite applications use the NORAD Simplified General Perturbations model 4 (SGP4) for satellite position prediction. The input of SGP4 consists of TLEs which are published via NORAD freely through the worldwide web. Since it is generated irregular, a new TLE which is independent of NORAD is required to access data about the satellite location each time. An alternative to independent computation of TLE can be made feasible by the existing emersion of GPS receivers in the space project and using IWO. Using this stochastic algorithm increases the success probability to find a more accurate TLE. The estimation is performed for six Keplerian orbital elements and SGP4 drag term ( B*) by using the satellite position data achieved from the GPS receiver. Also, the feasibility study of the concept is performed by using the actual GPS data of CHAMP and corresponding NORAD TLE. The time length of the GPS data used in the estimation is studied for practical application purposes. The results show that 500 samples of position data, with 1-min time intervals, are sufficient to keep the position error within 10 km in 3 days after epoch time in orbital elements with known drag term estimation. Moreover, for orbital elements with drag term estimation, 1000 samples of position, with 1-min time intervals, are sufficient to keep the position error within 15 km in 3 days after epoch time. If there is no considerable constraint for position-data time-length or time available for performing numerical computations, the estimated TLE can be more accurate than corresponding NORAD TLE.

Far-field pattern synthesis of small antenna arrays is a nontrivial problem. Typically radiation patterns are required to exhibit low sidelobe levels, whilst maintaining the ability to scan the main beam in space. Mutual coupling is a pertinent problem when the number of antenna elements is small, often precluding the use of conventional tapering algorithms. In this paper a technique previously employed to compensate for mutual coupling in linear arrays is extended to include small planar antenna arrays. The method consists of the calculation of the coupling coefficients between the array elements, which are then formed into a matrix and used to compensate for the effects of mutual coupling. It is then shown that this method is not suitable for all planar array configurations and a new technique is developed which allows the calculation of coupling matrices for these element configurations. Measured data from a small planar antenna array is presented, with the outlined methods employed to produce low sidelobe sum and difference beams.

A flexible approach based on a hybrid differential evolution strategy (HDES) is developed for adaptive pattern nulling with time modulated linear antenna arrays. The HDES adjusts the least significant bits of the digital phase shifters and the on-off time sequence to minimize the total output power, deep nulls can then be placed in the direction of the interferences. Thanks to the new design freedom introduced in time modulated antenna arrays, this adaptive nulling approach exhibits better performance over the traditional phase-only, amplitude-only, and phase-amplitude adaptive nulling methods, in terms of the practical implementation, nulling depth, and pattern deterioration. Several representative examples are presented to show the effectiveness of the proposed adaptive nulling approach.

This article aims to propose an alternate method of generating power-efficient sum-difference monopulse pattern using time-modulated antenna arrays. Time modulated antenna arrays have an added advantage of harmonics generation which can be controlled for simultaneous multipattern communication systems with suitable beamforming switching networks. In this work, a phase centered-based movement of predefined subarray cluster is applied in the proposed Beamforming Network (BFN) to produce simultaneous sum and difference patterns beneficial for monopulse radars. To enhance the efficiency of the switching network, a Single-Pole-Double-Throw (SPDT) based sequence is proposed. A 2N element symmetrically arranged linear antenna array is considered, and a suitable subarray cluster-controlled SPDT time scheme is implemented. The detailed comparison has been drawn between the phase-centered motion-based simple ON-OFF time scheme and the proposed enhanced SPDT-based harmonic BFN to show a superior monopulse performance.

This paper presents an efficient method for the pattern synthesis of linear antenna array based on the differential evolution algorithm. A number of examples are presented to demonstrate the various capabilities of the this method for different number of elements. The main objective of this paper is to achieve a set of current weights in order to steer the nulls in undesired directions (nulling the undesired signals) and steering the main beam towards the desired direction(direction of desired signal) by changing the phase excitation currents. The fitness function is obtained from maximization of error between actual pattern and desired pattern.