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ISAR imaging method and system based on time-frequency analysis of deep learning

A technology of time-frequency analysis and deep learning, applied in the radio wave measurement system, radio wave reflection/re-radiation, re-radiation utilization, etc., can solve the problems of low frequency resolution, generation of cross-terms, and poor effect, etc., to achieve Effects of increasing resolution, increasing frequency resolution, and suppressing cross-terms

Active Publication Date: 2019-02-15
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

When the motion of the target is too complex, the effect of the RD algorithm will become worse, and when the time-frequency analysis method is used, the improvement of the frequency resolution and the reduction of the cross term are contradictory
Although the WVD (Wigner-Ville distribution) method has a high frequency resolution, it will generate cross-terms, which seriously affect the quality of ISAR images. If the short-time Fourier transform (STFT) has no cross-terms, the frequency resolution is very low. Low

Method used

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  • ISAR imaging method and system based on time-frequency analysis of deep learning
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  • ISAR imaging method and system based on time-frequency analysis of deep learning

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Embodiment 1

[0093] As shown in the figure, the ISAR imaging method based on deep learning time-frequency analysis provided by this embodiment includes the following steps:

[0094] 1. Set the ISAR imaging model, obtain the echo data, and output the signal according to the following formula:

[0095]

[0096] in,

[0097] s(τ) represents the signal reflected from all points;

[0098] the s k (τ) represents the signal reflected from the kth point;

[0099] τ represents time; k represents the kth point;

[0100] means;; N is the number of scatterers, a k Indicates the intensity of the kth scatterer, f k is the Doppler center, γ k is the frequency modulation slope, the number of scatterers is assumed to be 3 in the experiment, a k all 1, f k is an integer conforming to U(-100,100) uniform distribution, γ k is an integer that conforms to the uniform distribution of U(-30,30);

[0101] 2. Perform WVD transform and short-time Fourier transform on the echo data to generate training d...

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Abstract

The invention discloses an ISAR imaging method based on time-frequency analysis of deep learning. Firstly, an ISAR imaging model is set up, and echo data is generated by simulation; secondly, WVD transformation and short-time Fourier transformation are carried out on the echo data to generate training data; thirdly, a network model is set up, and the network model is trained with the generated training data; and finally, the trained network model and the ISAR imaging model are combined for imaging. The method provided by the invention improves the frequency resolution of the time-frequency distribution map, can suppress cross-terms, and improves the resolution of ISAR imaging.

Description

technical field [0001] The invention relates to the technical field of radar imaging, in particular to an ISAR imaging method based on deep learning time-frequency analysis. Background technique [0002] The imaging processing method of synthetic aperture radar is one of the important issues in the research field. High-precision synthetic aperture radar images are of great significance for improving the accuracy of SAR interferometric phase extraction, image recognition accuracy, and expanding the application range of synthetic aperture radar; in order to obtain high-resolution Inverse Synthetic Aperture Radar (ISAR) images, the traditional approach is to use algorithms such as RD algorithm and time-frequency analysis. When the motion of the target is too complex, the effect of the RD algorithm will become poor, and when the time-frequency analysis method is used, the improvement of the frequency resolution and the reduction of the cross term are contradictory. Although the...

Claims

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Application Information

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IPC IPC(8): G01S13/90G01S7/41
CPCG01S7/417
Inventor 钱江黄少寅王开民张自文
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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