Flat-top beam forming method based on neural network

A beamforming method and neural network technology, applied in the field of microwave energy transmission, can solve problems such as difficult real-time adjustment and a large amount of time, and achieve a strong real-time effect

Active Publication Date: 2019-03-29
GUANGDONG UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

When the genetic algorithm, particle swarm optimization algorithm and other optimization algorithms are adjusted, it often takes a lot of time to complete the adjustment, and it is difficult to adjust in real time

Method used

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  • Flat-top beam forming method based on neural network
  • Flat-top beam forming method based on neural network
  • Flat-top beam forming method based on neural network

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Embodiment 1

[0059] With the development of modern technology, neural networks are widely used due to their powerful adaptive learning and generalization capabilities. Neural network has the ability to approximate nonlinear functions, which is based on the existence theory of Kolmogorov mapping network. The most widely used information processing operation in neural networks is the mathematical map, given an input vector X, the network should produce an output vector. Compared with traditional methods, the neural network does not require complex mathematical modeling and analysis, but only requires a large amount of data for training; at the same time, for a trained neural network, given an input, it can quickly calculate a result, which is more real-time. A neural network-based flat-hat beamforming method provided in this embodiment includes the following steps:

[0060] S1: collect sample data;

[0061]S2: Construct a convolutional neural network model;

[0062] S3: Use the sample dat...

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Abstract

The invention discloses a flat-top beam forming method based on a neural network. The method comprises the following steps: step S1, collecting sample data; step S2, constructing a convolutional neural network model; step S3, training the convolutional neural network model by using the sample data; and step S4, using the trained convolutional neural network mode to perform flat-top beam forming. According to the flat-top beam forming method based on the neural network provided by the invention, a closed-loop control is introduced in the flat-top beam forming, so that a system is more robust, and meanwhile, training is carried out by using the neural network, only a large amount of data are needed for training, and complex formula derivation and simplification are avoided. After the training is completed, an antenna is adjusted by using the trained neural network, a transmitting antenna and the amplitude of the transmitting antenna needing to be adjusted can be quickly determined according to the input change, and the real-time performance is strong.

Description

technical field [0001] The present invention relates to the field of microwave energy transmission, and more specifically, to a neural network-based flat-hat beamforming method. Background technique [0002] Antenna beamforming has been developed in radar and communication for more than 70 years, and is used in various occasions with different requirements. Common shaped beams include differential beams, cosecant beams, and flat-hat beams. Flat-hat beamforming has taken on new meaning in microwave energy transfer. In order to receive as much energy as possible, the antenna aperture at the receiving site is generally large. When the gain of the transmitting antenna is high, the power density distribution at the receiving site is no longer evenly distributed but is tapered and decreasing. When rectennas are distributed in an array for rectification, the efficiency is reduced. Therefore, in order to improve the receiving efficiency, it is necessary to perform flat-top beamfo...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04B7/06H04B7/08G06N3/04G06N3/08H01Q3/28
CPCH01Q3/28H04B7/0617H04B7/086G06N3/084G06N3/045
Inventor 刘义邱铭程晓洁卢毅
Owner GUANGDONG UNIV OF TECH
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