Sparse linear array optimization arrangement method under geographic constraint

Abstract

The invention provides a sparse linear array optimization arrangement method under geographic constraint, and belongs to the field of array signal processing. The invention aims to solve the problem of sparse linear array arrangement to establish a sparse linear array transmission signal model, establishes an objective function by taking the peak value side lobe level of the antenna directional diagram as an optimization standard, consides the actual condition in the array, and adds an antenna minimum spacing constraint and a geographic constraint to ensure that all the scanning angle peak value side lobe levels in the observation airspace are optimized to the minimum; compared with an original algorithm for solving the sparse linear array optimization problem, the membrane algorithm (MAPS) based on the P system has the advantages of being higher in convergence speed and good in convergence effect.

Description

technical field [0001] The invention belongs to the field of array signal processing, in particular to a sparse linear array optimization array method under geographical constraints. Background technique [0002] In the optimal layout of sparse linear array (Sparse Linear Array, SLA), the positions of the array elements are randomly placed according to a certain distribution. Compared with the uniform linear array, the aperture of the array is increased, the scanning beam is narrowed, the directionality is enhanced, and the spatial resolution is improved. , the mutual coupling effect between array elements is weakened, and grating lobes can be avoided in the pattern, so the optimal layout of sparse arrays has been extensively studied. Sparse array elements are placed irregularly, and the spatial sampling is non-uniform sampling. The array cannot achieve in-phase superposition on the far-field grating lobes, and the energy of the grating lobes can be effectively pressed into ...

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Problems solved by technology

Sparse array elements are placed irregularly, and the spatial sampling is non-uniform sampling. The array cannot achieve in-phase superposition on the far-field grating lobes, and the energy of the grating lobes can be effectively pressed into the side lobes, so the grating lobes can be eliminated, but it will bring Incoming pattern side lobe level increased

[0003] In order to overcome the shortcoming of the high sidelobe of the sparse array pattern, there is a sparse linear array optimization method, which considers the sum of the peak sidelobe levels (PSL) of the pattern under all scanning angles in the observation space, and takes it as the optimized The objective function, and then add array element size constraints and geographical constraints to the fixed aperture array element, and use the particle swarm optimization (PSO) algorithm to minimize the objective function value to solve the corresponding array element position

However, this method takes the sum of the peak sidelobe levels of all scanning angles as the optimization objective function, which cannot guarantee that the peak sidelobe levels of each scanning direction will reach the minimum; at the same time, the PSO algorithm has a slow convergence speed and is easy to fall into a local optimal solution

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