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A novel method based on alternating linear programming optimization is presented to synthesize sparse linear arrays with sum and difference patterns. This method can find the optimized element positions and excitations shared for both sum and difference patterns with accurate control of multiregion sidelobe distribution and difference pattern null slope. In addition, the constraints on the minimum element spacing and dynamic range ratio of the obtained excitations can be also appropriately incorporated into this alternating optimization process. Two synthesis examples with different pattern shape requirements are conducted to validate the effectiveness and advantages of the proposed method.

In this paper, we propose a proximal alternating direction method (PADM) for solving the convex optimization problems with linear constraints whose objective function is the sum of multi-block separable functions and a coupled quadratic function. The algorithm generates the iterate via a simple correction step, where the descent direction is based on the PADM. We prove the convergence of the generated sequence under some mild assumptions. Finally, some familiar numerical results are reported for the new algorithm.

This paper achieves the circular flat-top beam pattern synthesis of a uniform amplitude sparse planar dipole array by joint optimization of element rotations, positions and phases. Moreover, the minimum element spacing constraint for the synthesized array is considered by using an asymmetry mapping method (AMM). Importantly, on the basis of obtaining an equivalent or better pattern performance, the synthesized array can save 33.06% elements and avoid the usage of unequal power dividers than published results.

© 1963-2012 IEEE. This article presents a differential evolution algorithm with a new encoding mechanism and Cauchy mutation (DE-NEM-CM) for optimizing large unequally spaced planar array layouts with the minimum element spacing constraint. In the new encoding mechanism, each individual represents a certain element position rather than an entire array layout used in traditional stochastic optimization algorithms. Such an encoding mechanism has the following advantages: 1) in each individual updating, the array pattern can be efficiently evaluated by only considering the radiation contribution variation from one element movement, which can greatly reduce the computational time; 2) it naturally facilitates the generated new array layout in population updating to meet the minimum element spacing constraint, and 3) each individual is searched always in 2-D space as the array size increases. These advantages enable it to be very suitable for synthesizing large arrays. Besides, DE serves as a search engine, and Cauchy mutation with chaotic mapping is proposed to enhance the local search while preserving the diversity of the population. A set of experiments for synthesizing different types of unequally spaced planar arrays in both narrow-and broadband applications are conducted. Synthesis results show that the proposed method achieves much lower sidelobe level than some state-of-the-art stochastic optimization methods for all the test cases. Importantly, the proposed method is much more efficient than conventional stochastic optimization algorithm especially for the case of synthesizing large unequally spaced planar array layouts. A array layout optimization with more than 1000 elements can be achieved within acceptable CPU time cost, which has not yet been reported for the existing stochastic optimization methods without resorting to supercomputing facilities.

The Peaceman–Rachford splitting method (PRSM) is well studied for solving the two-block separable convex minimization problems with linear constraints recently. In this paper, we consider the separ...

The artificial bee colony (ABC) algorithm is one of the popular swarm intelligence algorithms that is inspired by the forging behavior of honeybee colonies. To improve the convergence precision of the ABC algorithm, accelerate the search speed of finding the best solution and control the balance between exploration and exploitation, we propose an improved double-population ABC algorithm based on heterogeneous comprehensive learning (HCLIABC). In this algorithm, the swarm is divided into exploration-subpopulation named group 1 and exploitation-subpopulation named group 2. Illuminated by particle swarm optimization (PSO), the food source will be updated on all dimensions rather than on a randomly selected dimension. Meanwhile HCL strategy is used to generate the exemplars for two subpopulations. In addition, opposition-based learning is used to improve the quality of initial swarm, and multiplicative weight update method is used to update the selection probability of the double-population in employed bees phase. To evaluate the remarkable performance of the improved algorithm, we conduct comparative experiments of 18 unimodal, multimodal, and rotated benchmark functions on dimensions 30 and 100. Computational results demonstrate that HCLIABC can effectively prevent premature convergence and produce competitive optimization precision and convergence speed compared with several popular and classic DE, PSO and ABC variants.

Synthesis of sparse arrays with reduced number of elements are significant for some applications where the available space, weight and the cost of the antenna system is very limited. In recent years, a variety of advanced techniques have been presented to deal with sparse array synthesis problems in either narrow and wideband cases. This paper presents a brief review of these techniques, and gives rough comparative study on some of sparse array synthesis methods.

© 2018 ACES. In this paper, a highly efficient approach is proposed to synthesize the low sidelobe pattern of uniformly spaced circular array. The proposed approach can be generalized to deal with the pattern synthesis for the circular array with directional elements. Numerical examples are given to verify the effectiveness and advantage of this approach.

A new shaped pattern synthesis method is presented in which element rotations, positions and phases are co-optimized to produce a shaped beam pattern for a sparse dipole array. Compared with conventional shaped pattern synthesis using excitation amplitude and phase optimization, the proposed method can not only reduce the number of elements But also avoid the usage of unequal power dividers. A synthesis example is provided to verify the performance of the proposed method.