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Matching dictionary and compressive sensing based radar range profile object identification method

A technology of matching dictionary and compressed sensing, which is applied in the field of one-dimensional range image target recognition based on compressed sensing theory

Inactive Publication Date: 2014-10-29
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

However, not all features can be effectively used for object recognition

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  • Matching dictionary and compressive sensing based radar range profile object identification method
  • Matching dictionary and compressive sensing based radar range profile object identification method
  • Matching dictionary and compressive sensing based radar range profile object identification method

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Embodiment Construction

[0037] The technical solution of the present invention will be described in detail below in combination with the embodiments and the accompanying drawings.

[0038] like figure 1 Shown, the implementation process of the present invention is specifically as follows:

[0039] S1. Perform data preprocessing, and divide the radar echo one-dimensional range images of different types of targets in the training samples according to the attitude angle of the radar echo one-dimensional range image, specifically: because the attitude of the one-dimensional range image is sensitive When the attitude angle of the target changes greatly, its one-dimensional range image will change greatly, so all the one-dimensional range images of the target are set up a module area at a certain attitude angle range, and the one-dimensional range image of the module area is taken Perform feature extraction to establish template vectors and combine them into a template vector library for the target. Set ...

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Abstract

The invention belongs to the technical field of automatic radar HRRP (high resolution range profile) object identification and particularly relates to compressive sensing based range profile object identification. Range profile object identification includes the steps: constructing a matching dictionary according to a radar echo model, selecting an appropriate test matrix for compressive sensing of a training sample range profile and to-be-identified test sample range profile which are known in type information so as to achieve data dimension reduction; then, subjecting data subjected to compressive sensing to sparse reconstruction so as to obtain sparse coefficients of the training sample range profile and the test sample range profile under the matching dictionary; utilizing the sparse coefficient of the training sample range profile as a pattern vector, and identifying the test sample range profile according to a nearest neighbor method. By the aid of compressive sensing based range profile object identification, since sparse coefficient characteristics of objects under the dictionary are extracted, redundancy is avoided, calculating amount is decreased and unnecessary noise is avoided.

Description

technical field [0001] The invention belongs to the technical field of radar high resolution one-dimensional range profile (High Resolution Range Profile, HRRP) automatic target recognition, in particular to one-dimensional range profile target recognition based on compressed sensing theory. Background technique [0002] Radar target recognition is to extract stable features from the scattered echoes of the target received by the radar, and automatically identify the attributes and types of the target to be recognized according to the existing target prior information. [0003] The radar target echo contains many features, such as the characteristic information of the target in the time domain, frequency domain and polarization domain. However, not all features can be effectively used for object recognition. Usually it is necessary to extract one or more features directly related to the target attribute from the target echo as the basis for target recognition, so as to effe...

Claims

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Application Information

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IPC IPC(8): G01S7/41
CPCG01S7/41
Inventor 周代英谭敏洁谭发曾贾继超田兵兵
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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