Antenna array sparse construction and directional diagram comprehensive method based on quantum bat searching

Abstract

The invention relates to an antenna array sparse construction and a directional diagram comprehensive method based on quantum bat searching. An overall optimal position of a scattered quantum bat group is obtained based on a quantum bat searching mechanism, and the overall optimal position is mapped as a sparse antenna array. On the basis of a constructed sparse antenna array, an overall optimal quantum position and an optimal mapping position of a continuous quantum bat group are obtained based on the quantum bat searching mechanism. Therefore, the optimal excitation amplitude of the antenna array is obtained.

Description

technical field [0001] The invention relates to an antenna array sparse construction and pattern synthesis method based on quantum bat search. Background technique [0002] Smart antenna technology can automatically adjust the transmission and reception pattern according to the combination of multiple antennas, and realize the optimization of parameter selection according to different application needs. It plays an important role in systems such as radar, wireless communication and electronic countermeasures. In actual engineering, the method of amplitude weighting is needed to improve the directivity of the antenna. In this way, the feeding network of the antenna will be very complicated, which will increase the system input cost and affect the processing speed of the system. The sparse construction of the antenna array can solve this problem to a certain extent. It uses one or more optimization methods to design the antenna system according to the specified pattern require...

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Problems solved by technology

[0003] In the prior art, "Invasive Weed Optimization Algorithm for Array Antenna Pattern Synthesis" published in "Journal of Xidian University" (2014, Vol.41, No.1, pp.29-34) by Liu Yan Adaptive standard deviation is used in this algorithm. This algorithm uses adaptive standard deviation to generate new seeds. According to the size of the individual fitness value of each parent generation, it is determined how far the new seeds generated are distributed from the parent generation. At the same time of global search speed and efficiency, it effectively balances global and local search capabilities, but it is easy to fall into local extremum

"Application of Particle Swarm Optimization Algorithm in Synthesis of Antenna Pattern" published in "Journal of Electronic Science and Technology University" (2011, Vol.40, No.2, pp.237-241) by Wang Weibo and others is based on hierarchical and multi-subgroup strategy, using the von Neumann neighborhood structure, while maintaining the diversity of the population, making full use of the effective information of its neighborhood individuals, an improved particle swarm optimization algorithm is proposed, and a fitness function suitable for its characteristics is adopted to better balance the The algorithm has global and local search capabilities, but this algorithm has slow convergence speed and is easy to fall into local extremum

[0004] The above two documents show that the existing antenna array pattern synthesis method only optimizes the excitation amplitude, but does not optimize the sparse position, which increases the input cost of the system and increases the failure rate of the system. The global convergence performance of the optimization algorithm for solving the antenna pattern synthesis problem is poor, and needs to be further improved

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