In this paper, firstly, we point out and correct a common mistake, in the literature, in the formulation of the array factor of an elliptical antenna array (EAA). Secondly, this paper deals with the optimal design of EAAs with low side lobes level using two recently proposed optimisation methods, namely, the Symbiotic Organisms Search (SOS) algorithm and the Antlion Optimisation (ALO) method. Two cases are considered. Firstly, the antennas are assumed to be evenly distributed over the ellipse contour, i.e. their positions are fixed, and the optimisation problem is to search for the current amplitudes ( $I_n$’s) that minimise the maximum side lobe level (SLL). Secondly, the antennas are assumed to be uniformly fed, i.e. I_n’s are assumed to be unity, and the optimisation problem is to search for the elements positions (along the ellipse) that minimise the maximum SLL. In both cases, SOS and ALO are used and their results are compared together. It is found that SOS is statistically better than ALO, though ALO consumes less CPU time to converge to the solution.

As a critical aviation support sensor, radar altimeters face imminent challenges due to the interference caused by the frequency overlap with the developing 5G telecommunications networks. This paper addresses the critical challenge posed by 5G interference with radar altimeter signals, which is crucial for maintaining aviation safety. It introduces a novel machine learning (ML) framework to preserve altimeter accuracy in the presence of 5G signals. This framework first classifies signals into pure or interfered categories, then applies regression models to predict altitudes when interference is detected, effectively quantifying and mitigating the interference impact. Distinguished by using real 5G signals from a Norman, Oklahoma base station, the approach offers a realistic evaluation and demonstrates the ML framework’s effectiveness in real-world conditions. Using various advanced ML models, the methodology showcases how critical aviation instruments can be safeguarded against the challenges posed by emerging telecommunications technologies, ensuring air travel safety and efficiency.

In this paper, firstly, a common mistake in the formulation of the array factor of concentric elliptical antenna array (CEAA) is pointed out and corrected. Secondly, several CEAAs are optimally designed to have the smallest side lobe level (SLL). This is accomplished using four optimization algorithms; Antlion Optimization algorithm (ALO), Grasshopper Optimization Algorithm (GOA), Symbiotic Organisms Search (SOS) and a newly proposed hybrid algorithm based on ALO and GOA. Two cases for CEAA synthesis are presented. The goal in the first case is to find the optimal values for the current amplitudes ( $I_n$’s) to minimize the side lobe level, assuming that the antennas’ positions are fixed along the ellipses. In the second case, the current amplitudes ( $I_n$’s) are assumed to be unity, and the optimization problem is to find the optimal values for the elements positions along the ellipses. The results of the proposed optimization algorithms are compared to uniform antenna array results.

The design of linear antenna arrays is one of the important electromagnetic optimization problems. In this paper, the problem of designing these arrays for specific radiation properties is dealt with. The Antlion optimization (ALO) and Grasshopper optimization algorithm (GOA) methods, which represent new evolutionary algorithms, are used in the optimization process. ALO and GOA are used to minimize the maximum side lobe level (SLL) by optimizing the excitation current amplitudes of array elements. The results obtained show the effectiveness of ALO and GOA compared to other optimization methods.

In this paper, a comparative and comprehensive study of synthesizing linear antenna array (LAA) designs, is presented. Different desired objectives are considered in this paper; reducing the maximum sidelobe radiation pattern (i.e., pencil-beam pattern), controlling the first null beamwidth (FNBW), and imposing nulls at specific angles in some designs, which are accomplished by optimizing different array parameters (feed current amplitudes, feed current phase, and array elements positions). Three different optimization algorithms are proposed in order to achieve the wanted goals; grasshopper optimization algorithms (GOA), antlion optimization (ALO), and a new hybrid optimization algorithm based on GOA and ALO. The obtained results show the effectiveness and robustness of the proposed algorithms to achieve the wanted targets. In most experiments, the proposed algorithms outperform other well-known optimization methods, such as; Biogeography based optimization (BBO), particle swarm optimization (PSO), firefly algorithm (FA), cuckoo search (CS) algorithm, genetic algorithm (GA), Taguchi method, self-adaptive differential evolution (SADE), modified spider monkey optimization (MSMO), symbiotic organisms search (SOS), enhanced firefly algorithm (EFA), bat flower pollination (BFP) and tabu search (TS) algorithm.