Pattern Synthesis of Antenna Arrays Using Dynamic Cooperative Grey Wolf Optimizer Algorithm

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.

In this work, a pattern synthesis method is presented for coupled antenna arrays via element rotation. The array is rigorously analyzed with a hybrid and full-wave analysis method based on the 3-D finite element method, modal analysis, generalized scattering matrix, and rotation and translation properties of spherical waves, including mutual coupling effects. The co- and cross-polar components are optimized by means of a gradient method, according to a specific cost function, via successive rotations of the elements.

A novel optimization method for multiple beamforming with null steering is proposed. Firstly, an invasive weed optimization method is improved in order to optimize the search depth. In the process of the seed diffusion, the crossover operation of genetic algorithm is introduced to modify the seed individuals, which can meliorate the searching ability and convergence speed of the algorithm. Then, the harmonic beamforming technology based on time modulated antenna array is presented, and multiple objectives about peak power and null steering are built. Finally, the improved invasive weed optimization algorithm is used in null forming based on pattern synthesis of antenna array, and the element excitation amplitude of time modulated antenna array is set to the excitation value obtained by the improved optimization algorithm. The simulation results show that the improved invasive weed optimization algorithm is superior to the conventional algorithm in performance. The simulation results show that forming null in the specified direction can be achieved while generating multiple beams.

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.

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.

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.

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.

Radiation pattern synthesis of a planar antenna array using relative elements axial rotation is proposed to generate broadside flattop or cosecant beams for surveillance, radars, and mobile communications. For a proof of concept, 5 x 5 planar Hertzian dipoles are utilized to synthesize all space-desired radiation patterns over a solid angle ratio of 13.4%. The rotation of each element is adjusted using a built-in MATLAB-based particle swarm optimization (PSO) technique. The deployed algorithm can be extended to include more elements and deploy different radiating elements.

An efficient search technique based on a touring ant colony optimization algorithm is presented for the shaped-beam pattern synthesis of a linear antenna array. The element excitations of equally spaced array and the element positions of uniformly excited array are determined to closely approximate the desired pattern. In the touring ant colony optimization algorithm, the frequency memory feature of tabu search is used. Illustrative examples of cosecant, flat-topped and pencil beam patterns are given to verify the validity and accuracy of the technique.

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.

Far-Field Radiation Pattern Synthesis of Elliptical Antenna Arrays Using GA and PSO

A flexible search technique based on a tabu search algorithm is presented for the shaped‐beam pattern synthesis of linear antenna arrays. To synthesize a desired pattern, element excitations and/or positions are optimally determined. Examples of cosecant, flat‐topped, and pencil beam patterns are given to illustrate the achievable performance. © 2002 Wiley Periodicals, Inc. Microwave Opt Technol Lett 36: 16–20, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.10657

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 novel method for the synthesis of arrays in the presence of conducting scatterers is presented. This method includes and takes advantage of the radiation of the scatterer by using the generalised multipole technique. Thus, the optimum solution can be obtained for an array in the presence of a conducting obstacle.