Near-field sparse array design method

Abstract

The invention belongs to the technical field of array antenna design, and relates to a near-field sparse array design method. The method comprises the following steps: firstly, according to a given initial uniform array, calculating a weight vector capable of realizing a near-field focusing beam, and taking the weight vector as a basis of a sparse array; and in the calculation process, firstly adopting a traditional genetic algorithm to carry out array design, after the genetic algorithm is carried out for a certain number of times, performing probability learning algorithm iteration before each turn of genetic algorithm is finished, and if a solution with higher fitness is generated within a certain number of learning times, replacing the optimal solution in the current genetic algorithm with the solution and is reserved. Compared with a traditional genetic algorithm and a greedy search algorithm, the method has the advantages that time complexity and design performance are well considered, and the method has certain flexibility. According to the method, high-performance global search can be carried out on a small array in actual engineering, and a better feasible solution is found.

Description

technical field [0001] The invention belongs to the technical field of array antenna design and relates to a near-field sparse array design method. Background technique [0002] With the development of array antenna technology, the near-field antenna array has become a hot topic under the background of the mature research of far-field antenna array. Near-field antenna arrays have been widely used in microphone arrays, wireless charging technology, radio frequency identification technology, sonar, radar and other fields, involving civilian, commercial and even military scenarios. In many studies, a uniform array with a half-wavelength element spacing is usually used as the basic model for research, and the array at this time is called a standard array. In the standard array, the beam pattern of the array has a better focusing effect, and because there are more elements in the array, the control of the energy is more flexible. In order to reduce resource overhead, some eleme...

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Problems solved by technology

Compared with the genetic algorithm, the search space of this algorithm is large enough, but because the entire solution space needs to be traversed in each search, its time complexity is high, which is not good for systems with strong real-time performance, especially for the number of array elements. When the array is larger, its adjustment to the array is lagging

[0005] Although the above-mentioned sparse array design methods can solve the problem of downsizing the array in near-field beamforming to a certain extent, the two types of methods have some deficiencies, and it is difficult to achieve a good balance in terms of performance and time complexity.

When high beam performance is required in the array design, in order to ensure a sufficiently large search space, the corresponding algorithm often has a large time complexity; in order to run in a real-time system, a solution with a low time complexity needs to be adopted. At this time, the algorithm Insufficient coverage of the search space may lead to performance degradation and affect the final beam effect of the array

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