Unequally spaced linear arrays are synthesised for the sidelobe level (SLL) suppression, the null control in some specified directions and the beamwidth (BW) set within predefined limits. Based on the vectormapping m...
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Unequally spaced linear arrays are synthesised for the sidelobe level (SLL) suppression, the null control in some specified directions and the beamwidth (BW) set within predefined limits. Based on the vector mapping method and the position perturbation technique, the constrained vector projection (CVP) is defined to offer flexibility in controlling null depths with multiple constraints. A modified differential evolution (DE) algorithm with the CVP method (in short MDE-CVP) is then performed in the array synthesis, which effectively reduces the optimisation space, avoids the infeasible solutions and enhances the global search ability. Three examples are presented and the first two results are compared with those obtained by learning particle swarm optimisation, invasive weed optimisation and wind-driven optimisation and a modified version of the central force optimisation algorithms. Comparison results indicate that the MDE-CVP algorithm outperforms the existing algorithms in terms of minimum SLL, BW control, null control and convergence characteristics.
Sparse method provides an efficient way for antenna systems to achieve low cost, weight, and complexity. However, the synthesis of sparse arrays always limited to multiple constraints, which include aperture, number o...
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Sparse method provides an efficient way for antenna systems to achieve low cost, weight, and complexity. However, the synthesis of sparse arrays always limited to multiple constraints, which include aperture, number of elements, and minimum elements spacing. The characteristic that element number in each ring of concentric ring array (CRA) is related to its ring radius, which is different from square or rectangle planar arrays, leads to a complex constrained optimisation problem for the synthesis. Here, a novel vector mapping method for e-design of sparse CRA is proposed. According to the mapping functions established here, the ring radius and element number can be mapped by two variable vectors with only upper and lower boundaries. Thus, the strong constrained optimisation problem can be transformed to the unconstrained. Numerical examples show the effectiveness of the proposed method, which provides layouts of elements able to produce lower sidelobe level that satisfy multiple requirements.
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