Method and device for improving electromagnetic property measurement precision of equipment

A technology of electromagnetic characteristics and measurement accuracy, applied in the direction of neural learning methods, constraint-based CAD, complex mathematical operations, etc., to achieve the effect of efficiency improvement, robust and reliable solution capabilities

Pending Publication Date: 2022-06-10
NAT UNIV OF DEFENSE TECH
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  • Abstract
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Problems solved by technology

[0002] Electromagnetic scattering and inverse scattering are the core issues of cutting-edge technologies such as electromagnetic stealth equipment design and target electromagnetic imaging recognition. The complexities brought about by problems such as scale, strong ill-conditioning, noise interference during signal acquisition, and information gaps in key technical indicators of non-cooperative targets have always restricted the development of efficient numerical algorithms and simulation tools
In terms of electromagnetic scattering problems, the traditional low-frequency numerical method has low solution efficiency and is not suitable for large-scale simulation; the high-frequency approximation method is fast and easy to solve large-scale problems, but has poor accuracy and is only suitable for simple structural targets; in terms of electromagnetic inverse scattering, the current Some algorithms have limitations such as low algorithm accuracy, slow solution, blurred imaging, and not suitable for high-contrast targets.
[0003] The applicant found that there are at least the following problems in the prior art: the efficiency and accuracy of the solution of electromagnetic forward and reverse scattering are not high

Method used

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  • Method and device for improving electromagnetic property measurement precision of equipment
  • Method and device for improving electromagnetic property measurement precision of equipment
  • Method and device for improving electromagnetic property measurement precision of equipment

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Embodiment 1

[0063] Affected by factors such as manufacturing process accuracy, equipment wear and tear, and storage conditions, there are uncertainties in the electromagnetic characteristics of equipment during the life cycle, which restricts the long-term reliability of equipment. The amount of data collected by the weapon equipment system is huge, the signal-to-noise ratio is low, data is missing, there are many factors affecting the maintenance of electromagnetic characteristics in the life cycle of equipment, and the mathematical model based on physics and statistics is not perfect. difficulty. The application of forward and reverse scattering fusion models and statistical analysis tools is expected to solve these technical problems. Based on the physical background and statistical principles of forward and reverse scattering, a comprehensive analysis platform compatible with various experimental data such as simulation, target test, and exercise is built to realize the uncertainty ev...

Embodiment 2

[0066]

[0067] Among them, u represents the deep neural network obtained by training, X, f, and g represent the known target electromagnetic scattering characteristics and electromagnetic scattering background, the training set composed of experimental measurement samples or numerical simulation results, and the prior physical knowledge introduced by the electromagnetic equation. Represents the manually selected loss function in the model. Due to the impossibility of infinite sample size and computing resources, etc., in actual calculations, the above ideal model is often approximated by solving the following problems:

[0068]

[0069] In the specific implementation, the feedforward neural network and error backpropagation are generally used for model training. The project uses the incident data of the emission source and the electromagnetic characteristic distribution of the space to be solved as input, and uses methods such as data-driven, active learning or unsuperv...

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Abstract

The embodiment of the invention provides a method and device for improving equipment electromagnetic characteristic measurement precision, and the method comprises the following steps: building a neural network model of a Maxwell equation based on a deep learning solving model of an electromagnetic equation in combination with a neural network, the method specifically comprises the following steps: receiving emission source data and a space electromagnetic characteristic distribution value solved by a deep learning solution model of an electromagnetic equation; transmitting source data and the space electromagnetic characteristic distribution value are input into a neural network; combining the emission source data and the spatial electromagnetic characteristic distribution value by using a neural network, and establishing a neural network model of a Maxwell equation; solving the electromagnetic characteristic distribution of the equipment space by using a neural network model of a Maxwell equation; and correcting the solution through a neural network model of a Maxwell equation. Compared with the current mainstream electromagnetic scattering and inverse scattering numerical method, the efficiency is improved by more than 20%, the super-resolution reconstruction of the detected target structure is realized, and the inverse scattering imaging resolution is wholly superior to that of the mainstream method.

Description

technical field [0001] The invention relates to the field of detection technology, in particular to a method and a device for improving the measurement accuracy of electromagnetic characteristics of equipment. Background technique [0002] Electromagnetic scattering and inverse scattering are the core issues of cutting-edge technologies such as electromagnetic stealth equipment design and target electromagnetic imaging recognition. The strong coupling, strong nonlinearity, multi-scale, and strong ill-conditioning of the mathematical model, as well as noise interference during signal acquisition and non-cooperative targets The complexity brought about by issues such as information gaps in key technical indicators has always restricted the development of efficient numerical algorithms and simulation tools. In terms of electromagnetic scattering problems, the traditional low-frequency numerical method has low solution efficiency and is not suitable for large-scale simulation; t...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/27G06N3/04G06N3/08G06F17/13G06F111/10G06F111/04
CPCG06N3/084G06F17/13G06F30/27G06F2111/10G06F2111/04G06N3/045
Inventor 刘子源王海峰包凯君钱旭张弘
Owner NAT UNIV OF DEFENSE TECH
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