RCS near-far field transformation method based on deep neural network

A deep neural network and neural network technology, which is applied in the field of RCS near-far field transformation based on deep neural network, can solve the problems affecting the accuracy and error of the algorithm, and achieve the effect of improving the accuracy, overcoming the numerical error and simplifying the training process.

Active Publication Date: 2018-12-11
BEIJING INSTITUTE OF TECHNOLOGYGY
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Problems solved by technology

Mathematically, the relationship between near-field scattering measurement data and far-field RCS can be solved, but in engineering implementation, since the actual echo signals are all

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  • RCS near-far field transformation method based on deep neural network
  • RCS near-far field transformation method based on deep neural network
  • RCS near-far field transformation method based on deep neural network

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Embodiment Construction

[0024] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. The present invention includes but is not limited to the following examples.

[0025] The invention proposes a near-far field transformation method based on a deep neural network. The core idea is to fit the relationship between near-field scattering data and far-field scattering data through deep learning, so as to realize near-far field transformation. The specific process is as follows: figure 1 shown.

[0026] Most of the targets to be measured in reality are multi-scattering center targets, and their overall RCS can be equivalent to the mutual superposition of each basic scattering center. The RCS of the target can be expressed as

[0027]

[0028] Formula (1) is defined under far-field conditions, j is the imaginary unit, f is the test frequency, c is the speed of light, the target is composed of N scattering points, and the R...

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Abstract

The invention discloses an RCS near-far field transformation method based on a deep neural network, which comprises the steps of first, selecting a neural network according to radar echo data measuredin the near field, wherein a feed-forward neural network is selected if the radar echo data is single-frequency-point data, and a convolutional neural network is selected if the radar echo data is multi-frequency-point data; second, obtaining near-field RCS data and corresponding far-field RCS data to serve as training samples, wherein the near-field RCS data serves as input of the neural network, an output expected result is compared with the generated far-field RCS data, the neural network is trained through an error back-propagation algorithm, and a neural network conforming to error requirements is obtained through adjusting control parameters of the neural network; and third, inputting the RCS data measured in the near field into the trained neural network in actual transformation soas to obtain the transformed far-field RCS data. The RCS near-far field transformation method reduces numerical values caused by the discretization in implementation of the traditional algorithm, andis a brand new RCS near-far field transformation method.

Description

technical field [0001] The invention belongs to the field of microwave measurement, and in particular relates to an RCS (radar cross section) near-far field conversion method based on a deep neural network. Background technique [0002] Stealth technology, as an advanced technology verified in actual combat in recent years, has long become a hot spot for research by various countries. The core goal of stealth is to reduce the radar cross section (RCS) of the target by various means. Stealth technology has become a technology widely used in weapons and equipment systems around the world. Stealth technology has been widely used in various weapons and equipment systems such as aircraft and missiles. [0003] The development of stealth technology must be inseparable from the corresponding measurement technology, so the measurement technology of stealth performance based on radar cross section (RCS) has important reference value for the development of stealth technology. Accord...

Claims

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Application Information

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IPC IPC(8): G01S13/89G01S7/40
CPCG01S7/40G01S13/89
Inventor 胡伟东刘阳张文龙孙健航吕昕
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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