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Extrapolation Method of Range Image Data Based on Sparse Scattering Center Extraction

A scattering center and data extrapolation technology, applied in the field of data processing, can solve problems such as small number of samples, difficulty in obtaining recognition results, and affecting the accuracy of statistical models, so as to achieve the effect of improving recognition rate and ensuring accuracy

Active Publication Date: 2017-06-16
XIDIAN UNIV
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Problems solved by technology

[0006] However, in actual situations, due to the non-cooperative nature of the target, it is difficult for the radar to obtain the continuous echoes of the target in a long period of time, and the number of samples that can be used to estimate the model parameters is very small, which affects the accuracy of the statistical model. Difficult to get ideal recognition results

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  • Extrapolation Method of Range Image Data Based on Sparse Scattering Center Extraction
  • Extrapolation Method of Range Image Data Based on Sparse Scattering Center Extraction
  • Extrapolation Method of Range Image Data Based on Sparse Scattering Center Extraction

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[0029] refer to figure 1 , the specific implementation steps of the present invention are as follows:

[0030] Step 1, acquisition and framing of HRRP samples:

[0031] In actual situations, due to the non-cooperative nature of most targets, the complex HRRP data acquired by the radar is often missing. The present invention divides the obtained complex high-resolution range image HRRP into frames according to the angle domain, and divides the data in the angle domain α The continuous complex HRRP samples are defined as a frame of data, where, B represents the radar bandwidth, L represents the horizontal dimension of the target, and c represents the speed of light.

[0032] Step 2, perform ISAR imaging on each frame of data.

[0033] Taking the measured data of the aircraft target Jacques as an example, the dimension of the range image is 256, and each frame of data contains a total of 32 complex HRRP samples, so the dimension of the ISAR image is 256×32. Dimension inverse...

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Abstract

The invention discloses a range profile data extrapolation method based on extraction of a sparse scattering center, and aims at solving the problem that the target identification result is low when the amount of samples is relatively few in the prior art. The method comprises that (1) angle enframing is carried out on obtained multi-HRRP data; (2) ISAR imaging is carried out on each frame of multi-HRRP data, and a position set of the scattering center is obtained according to ISAR images; (3) a dictionary is generated according to the position set of the scattering center; (4) the dictionary is used to express each frame of data; (5) the sparsity of each frame of data is solved to obtain a target parameter combination of the scattering center; (6) a multi-HRRP sample which is extrapolated after each frame is obtained according to the target parameter combination of the scattering center; and (7) IFFT is carried out on each frame of data to obtain a time-domain real HRRP sample which is extrapolated after each frame, and the time-domain real HRRP sample is used for subsequent target identification. The method of the invention can substantially improve the target identification rate and be used for radar HRRP identification.

Description

technical field [0001] The invention belongs to the technical field of data processing, relates to data extrapolation of samples in a small angle domain, and can be used to solve target recognition. Background technique [0002] When the size of the RLOS of the target along the radar line of sight is much larger than the range resolution of the radar, the target cannot be treated as a "point". Since the target occupies multiple range resolution units, the fluctuations and peaks in the corresponding radar echo reflect the distribution of target scattering points along the distance direction. These echoes are called high-resolution range profile HRRP. [0003] In recent years, HRRP-based radar target recognition technology has attracted more and more attention from various countries due to its unique advantages. It can be divided into two categories: HRRP recognition methods based on feature extraction and kernel function classifiers and statistical modeling. [0004] 1) The ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01S7/41
CPCG01S7/41
Inventor 杜兰李宁和华王鹏辉王家东胡靖
Owner XIDIAN UNIV
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