A Multi-objective Array Pattern Optimization via Thinning Approach

Abstract

In this paper, the possibility of synthesizing a linear antenna array for multiple objectives with the thinning approach is demonstrated. The thinning space is constrained to three cases (side, central, and random) parts instead of a fully filled linear array. In the case of the side part, a set of elements located on both edges of the array are removed with the optimized elements close to the center remaining unchanged. As in the case of the central part, only a set of elements close to the center are removed. In the case of a random selection of elements, the cancellation process is carried out randomly within the sides and the center. Since the amplitude weights of the elements located on the edges of the array have a small amplitude excitation, the method of side thinning gives better results than the other two cases. Moreover, in cases of side and random thinning, the last element of each side is excluded from the thinning process to maintain the aperture size. The convex algorithm (CA) is used to perform such thinning optimization. CA optimization efficiently computes a multi-objective function in coordination with the thinned array technique, such as preserving the main beamwidth in all cases with the reduction of sidelobe levels, generating one or more nulls, and steering the main beam in a certain direction. The simulation results, in all cases, show that 30%-40% of the array elements can be turned off with achieving a multi-objective radiation pattern.

Different 2D and 3D mask constraints synthesis for large array pattern shaping

“…The maximum number of iterations can be chosen through the proposed cost function. After initializing the algorithm, a final cost function is added to form the radiated beams, known as a mask (envelope) that is determined at different levels according to the imposed constraints: ML(u) lower level mask ≤ |LAF (u)| ≤ MU(u) upper level mask (13) ML(u, v) lower surface mask ≤ |PAF (u)| ≤ MU(u, v) upper surface mask (14) The basis of the PSO algorithm is to reduce the square root value of the component F x :…”

“…In fact, without imposing any restrictions on the array factor in terms of excitation distribution or array shape, the problem is depicted as a convex problem that can be improved either by using convex algorithms or evolutionary algorithms. By doing so, the optimal compromise can be successfully addressed by optimizing patterns based on amplitude only, phase only, or both for single-pulse radar communications [12,13].…”

“…In paper [30], the method of formation of a pattern using Taylor with superposition primitives based on a genetic algorithm (GA) with control of the sidelobe levels (SLLs) has been presented. In papers [12,13,20,25], the researchers proposed a set of methods for installing sub-arrays by controlling some characteristics of the coverage pattern in addition to reducing the complexity of the system from a practical point of view. In addition to many well-known classical methods such as the Fourier transform, Chebyshev polynomials, and Schelkunoff as these methods deal with excitation (amplitude and phase) levels.…”

Low Complexity Irregular Clusters Tiling through Quarter Region Rotational Symmetry

“…It is known that approximately half of the complexity of the array system is due to the transceiver modules (T/R units) that control the amplitude and time delay of each radiating element to perform the desired radiation beamforming [1]. To reduce complexity while still providing satisfactory radiometric features, several untraditional architectures such as clusters [2][3][4], thinning [5], sparse arrays [6], amplitudeonly [7], and phase-only [8] have been proposed instead of optimization using all-element solutions. Such proposals are gaining great attention from researchers to form the required patterns in the fifthgeneration communication applications and modern radar systems [9].…”