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This authoritative resource provides you with a detailed description of ideal array element characteristics that help you estimate the quality of development of real-world phased array antennas. You find several approaches to optimum phased array design, allowing you to provide specified array gain in a specific region of scan, using a minimum number of expensive, controlled devices. Moreover, this practical book presents important numerical methods that you can use to model and optimize phased array structure to obtain the best array characteristics that the chosen structure can provide.From arrays with beam-forming networks, arrays of coupled dual-mode waveguides, and arrays with reactively loaded radiators, to waveguide arrays with protruding dielectric elements, and arrays with strip, disk, and wire structures, this comprehensive reference explains a wide range of essential topics to help you with work in this challenging area. The book is supported with over 165 illustrations and more than 566 equations.

Considering systems engineering of phased arrays and addressing not only radar, but also these modern applications, this book presents a system-level perspective and approach that is essential for the successful development of modern phased arrays. --

Meta-heuristic optimization algorithms have seen significant advancements due to their diverse applications in solving complex problems. However, no single algorithm can effectively solve all optimization challenges. The Naked Mole-Rat Algorithm (NMRA), inspired by the mating patterns of naked mole-rats, has shown promise but suffers from poor convergence accuracy and a tendency to get trapped in local optima. To address these limitations, this paper proposes an enhanced version of NMRA, called Salp Swarm and Seagull Optimization-based NMRA (SSNMRA), which integrates the search mechanisms of the Seagull Optimization Algorithm (SOA) and the Salp Swarm Algorithm (SSA). This hybrid approach improves the exploration capabilities and convergence performance of NMRA. The effectiveness of SSNMRA is validated through the CEC 2019 benchmark test suite and applied to various electromagnetic optimization problems. Experimental results demonstrate that SSNMRA outperforms existing state-of-the-art algorithms, offering superior optimization capability and enhanced convergence accuracy, making it a promising solution for complex antenna design and other electromagnetic applications.

In this paper, a novel swarm intelligent algorithm is proposed called ant nesting algorithm (ANA). The algorithm is inspired by Leptothorax ants and mimics the behavior of ants searching for positions to deposit grains while building a new nest. Although the algorithm is inspired by the swarming behavior of ants, it does not have any algorithmic similarity with the ant colony optimization (ACO) algorithm. It is worth mentioning that ANA is considered a continuous algorithm that updates the search agent position by adding the rate of change (e.g., step or velocity). ANA computes the rate of change differently as it uses previous, current solutions, fitness values during the optimization process to generate weights by utilizing the Pythagorean theorem. These weights drive the search agents during the exploration and exploitation phases. The ANA algorithm is benchmarked on 26 well-known test functions, and the results are verified by a comparative study with genetic algorithm (GA), particle swarm optimization (PSO), dragonfly algorithm (DA), five modified versions of PSO, whale optimization algorithm (WOA), salp swarm algorithm (SSA), and fitness dependent optimizer (FDO). ANA outperformances these prominent metaheuristic algorithms on several test cases and provides quite competitive results. Finally, the algorithm is employed for optimizing two well-known real-world engineering problems: antenna array design and frequency-modulated synthesis. The results on the engineering case studies demonstrate the proposed algorithm’s capability in optimizing real-world problems.

The Global Navigation Satellite System (GNSS) has emerged as a critical spatiotemporal infrastructure for ensuring the integrity of remote sensing data links. However, traditional GNSS antenna arrays, typically configured with the antenna spacing of half a wavelength, are constrained by the spatial limitations of remote sensing platforms. This limitation results in a restricted number of interference-resistant antennas, posing a risk of failure in scenarios involving distributed multi-source interference. To address this challenge, this paper focuses on the multidimensional trade-off problem in the design of compact GNSS anti-interference arrays under finite spatial constraints. For the first time, we systematically reveal the intrinsic relationships and game-theoretic mechanisms among key parameters, including the number of antennas, antenna spacing, antenna size, null width, coupling effects, and receiver availability. First, we propose a novel null width analysis method based on the steering vector correlation coefficient (SVCC), elucidating the inverse regulatory mechanism between increasing the number of antennas and reducing antenna spacing on null width. Furthermore, we demonstrate that increasing antenna size enhances the signal-to-noise ratio (SNR) while also introducing trade-offs with mutual coupling losses, which degrade SNR after compensation. Building on these insights, we innovatively propose a multi-objective optimization framework based on the non-dominated sorting genetic algorithm-II (NSGA-II) model, integrating antenna electromagnetic characteristics and signal processing constraints. Through iterative generation of the Pareto front, this framework achieves a globally optimal solution that balances spatial efficiency and anti-interference performance. Experimental results show that, under a platform constraint of 1 wavelength × 1 wavelength, the optimal number of antennas ranges from 15 to 17, corresponding to receiver availability rates of 89%, 72%, and 55%, respectively.

An antenna array with short shielded transverse electromagnetic horns (S-TEM-horns) for emitting high-power radiation of ultra-short electromagnetic pulses (USEMP) has been created and researched. The antenna unit consists of an ultra-wideband antenna array with four S-TEM horns, with each connected to a two-wire HF transmission line, and these four lines are connected to an antenna feeder. This feeder is connected to a semiconductor generator with the following parameters: a 50 Ohm connector, 10–100 kV high-voltage monopolar pulses, a rise time of about 0.1 ns, FWHM = 0.2–1 ns, and pulse repetition rates of 1–100 kHz. The antenna array was designed and optimized to achieve a high efficiency of about 100% for the antenna aperture by using a 2 × 2 array with S-TEM-horns, with shielding rectangular plates for the return current. The transient responses were studied by simulation using the electromagnetic 3D code “KARAT” at the time domain and experimentally with the use of our stripline sensor for measurement of the impulse electrical field with a 0.03 ns rise time and a 7 ns duration at the traveling wave. The radiators were emitting USEMP waves with a hyperband frequency spectrum of 0.1–6 GHz. The radiation with an amplitude of 5–30 kV/m of the E-field strength at a distance of up to 20 m was successfully applied to test the electronics for immunity to electromagnetic interference.

Firefly algorithm (FA) is a nature inspired computing algorithm based on the behaviour of fireflies. It is a kind of stochastic meta-heuristic algorithm that can be used for various engineering optimisation problems. This study presents application of FA for the design of thinned multiple concentric circular antenna arrays. The aim is to achieve an array of uniformly excited isotropic elements that will generate a pencil beam pattern in the vertical plane with minimum side lobe level (SLL). Two different cases have been considered in this study for thinning of concentric circular (ring) arrays using FA. The first case is with uniform inter-element spacing fixed at 0.5λ or its multiple and the second case with optimum inter-element spacing or its multiple. The thinning percentage of the array is kept equal to or more than 50% and the beamwidth is kept equal to or less than that of a fully populated, uniformly excited and 0.5λ spaced ring array of same number of elements and rings. The results obtained are compared with previous published results, which show the effectiveness of this approach.

An original advanced level reference appealing to both the microwave and antenna communities * An overview of the research activity devoted to the synthesis of transmission lines by means of electrically small planar elements, highlighting the main microwave applications and the potential for circuit miniaturization * Showcases the research of top experts in the field * Presents innovative topics on synthesized transmission lines, which represent fundamental elements in microwave and mm-wave integrated circuits, including on-chip integration * Covers topics that are related to the microwave community (transmission lines), and topics that are related to the antenna community (phased arrays), broadening the readership appeal

When designing radar, mobile, or satellite communication systems, optimization problems often come out and need to be handled to accomplish certain requirements. Polyphase code design and circular antenna array design problems are considered in this paper. To deal with the two problems, a novel artificial bee colony (ABC) algorithm is proposed. A population reduction method is used in the proposed algorithm. Large population is initially set for exploration search, while population is reduced to a small one for exploitation search. Moreover, a new boundary repair method is proposed to amend the candidate solutions that violate boundary constraints. It hybridizes four popularly used repair methods in literature. The resulting algorithm is called population reduction and hybrid repair ABC (PRHRABC). Experiments are conducted on the two design problems. Results show that PRHRABC presents promising performance in dealing with the problems compared with standard and a state-of-the-art ABC algorithms.