Adaptive Uniform Circular Array Synthesis Using Cuckoo Search Algorithm

Abstract

Naturally suited array geometry for 360˚ coverage is the uniform circular array (UCA). A comparison of two types of uniform circular array configurations is presented in this paper. Due to its symmetrical geometry UCA is always targeted which results in minimal change inside lobe levels and beam width when scanned by a phased array antenna. Particle Swarm Optimization and Cuckoo algorithm are used for the calculation of complex weights of the array elements. Comparisons are drawn in the context of adaptive beam forming capabilities. Obtained results suggest that planar uniform circular array (9:10) using Cuckoo algorithm, has better beam forming properties with also reduced side lobe levels when compared to other geometry.

Synthesis for sidelobe suppression of linear array based on improved grasshopper optimization algorithm with adaptive chaotic strategy

“…After 20 independent runs, the maximum SLL convergence characteristics of the 16‐element linear array optimized by GOA and ACGOA are shown in Figure 23, compared with GOA, ACGOA can find the best global optimum more quickly, the convergence speed is faster and the trend is more stable, and the optimization value of SLL is smaller. The array factor optimized by FA, 18 CS, 20 IWO, 24 CABMO, 25 GOA, and ACGOA are shown in Figure 24. The maximum SLL of the 16‐element linear array optimized by each algorithm and the excitation amplitude of each element are listed in Tables 11 and 12, respectively.…”

“…For antenna optimization in electromagnetic field, although many classical methods (convex optimization, 3,4 FFT, 5,6 and alternative projection 7,8 based methods) have been applied to array antenna sidelobe suppression, the most popular stochastic optimization method is the population algorithm inspired by nature. In the aspect of linear array pattern synthesis, there are many successful swarm intelligence optimization algorithms, such as genetic algorithm (GA), 9,10 particle swarm optimization (PSO), 11,12 cuckoo optimization algorithm (COA), 13 cat swarm optimization (CSO), 14 spider monkey optimization (SMO), 15 whale optimization algorithm (WOA), 16 firefly algorithm (FA), [17][18][19] cuckoo search (CS), 20,21 butterfly mating optimization (BMO), 22,23 chaos adaptive butterfly mating optimization (CABMO), 24 invasive weed optimization (IWO), 25 improved harmony search algorithm (IHSA), 26 etc. Because of their good robustness, the optimization ability of these algorithms has been verified to a certain extent, but there are still some limitations for specific antenna problems, such as convergence, optimization speed and accuracy, local or global optimization ability, etc.…”

Optimal Pattern Synthesis of Linear Array and Broadband Design of Whip Antenna Using Grasshopper Optimization Algorithm

“…CSO [13] SMO [14] GOA Figure 10, the distribution of maximum SLL for 20 runs of GOA for 16-element linear array is shown in Figure 11, and the best array factor is obtained as shown in Figure 12, which is compared with FA [13,[23][24][25], CS [13,26,27], IWO [13], and CABMO [28] optimized arrays. e maximum SLL and the excitation magnitude of each element of the linear array optimized by each algorithm are shown in Table 6.…”

“…In terms of electromagnetic field problems and antenna optimization, population-based algorithms inspired by nature are the most popular in stochastic optimization methods [4,5]. Many swarm intelligent algorithms have been successfully applied to antenna array pattern synthesis or antenna broadband optimization, such as genetic algorithm (GA) [6,7], ant colony optimization (ACO) [8,9], particle swarm optimization (PSO) [10][11][12], invasive weed optimization (IWO) [13], cat swarm optimization (CSO) [14], spider monkey optimization (SMO) [15], butterfly mating optimization (BMO) [16,17], social group optimization (SGO) [18], grey wolf optimization (GWO) [19], quadratic programming method (QPM) [20], flower pollination algorithm (FPA) [21], ant lion optimization (ALO) [22], firefly algorithm (FA) [23][24][25], cuckoo search (CS) [26,27], chaotic adaptive butterfly mating optimization (CABMO) [28], modified spider monkey optimization (MSMO) [29], enhanced firefly algorithm (EFA) [30], bat flower pollination (BFP) algorithm [31], gravitational search algorithm (GSA) [32], and so on. For antenna broadband optimization, there are also GA [33,34], evolutionary algorithm (EA) [35], real frequency technology [36], IWO [37][38][39], etc.…”

Robust Adaptive Sidelobe and Interference Suppression for Uniform Circular Array Antenna Based on Back-propagation Neural Network

“…Therefore, it generally suffers from sidelobe interference. In order to enhance the directivity, many algorithms have been proposed to suppress sidelobe interference, such as Minimum variance distortionless response (MVDR), Least mean square (LMS) algorithm, cuckoo search algorithm, Recursive least square (RLS) algorithm [8] [9]. As in [10] and [11], Utilizing the phase mode transformation technique (UCA) techniques are shown to accomplish equal side lobe reduction.However, this method requires many modes; therefore, its precise application is inapplicable.…”