Linear discriminant learning true and false target one-dimensional range profile feature extraction method

A linear discrimination and feature extraction technology, applied in radio wave measurement systems, instruments, etc., can solve the problem of the degradation of the recognition performance of the discriminant vector subspace method, and achieve the effect of overcoming the defects of the conventional discriminant vector subspace and improving the classification performance.

Active Publication Date: 2019-07-12
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

[0003] However, the discriminant vector subspace method is only suitable for the case where the sample data is a Gaussian distribution, and the actual sample data distribution may be non-Gaussian distribution, resul...

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  • Linear discriminant learning true and false target one-dimensional range profile feature extraction method
  • Linear discriminant learning true and false target one-dimensional range profile feature extraction method
  • Linear discriminant learning true and false target one-dimensional range profile feature extraction method

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Embodiment Construction

[0030] In order to verify the effectiveness of the proposed method, the following simulation experiments are carried out.

[0031] Four point targets are designed: true target, fragment, light bait, and heavy bait. The bandwidth of the radar emission pulse is 1000MHZ (the distance resolution is 0.15m, the radar radial sampling interval is 0.075m), the target is set as a uniform scattering point target, the scattering points of the real target are 7, and the scattering points of the other three targets are all 11 . In the one-dimensional range images with target attitude angles ranging from 0° to 90° at intervals of 1°, take the one-dimensional distances with target attitude angles of 0°, 2°, 4°, 6°, ..., 90° The one-dimensional range images of other attitude angles are used as test data, and there are 45 test samples for each type of target.

[0032] For four kinds of targets (true targets, fragments, light decoys and heavy decoys), within the range of attitude angles from 0...

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Abstract

The invention belongs to the technical field of radar target recognition, and particularly relates to a linear discriminant learning true and false target one-dimensional range profile feature extraction method. According to the method, various sample distribution center vectors are obtained through iterative learning; then, a learning center vector is used for replacing a sample mean vector to calculate an intra-class scattering matrix and an inter-class scattering matrix; the linear discriminant learning transformation matrix is obtained through calculation, the degree of intra-class aggregation and inter-class separation can still be well represented under the condition that target sample data distribution is non-Gaussian distribution, the defect that a conventional discriminant subspace is only suitable for sample data Gaussian distribution is overcome, and therefore the target recognition performance is improved.

Description

technical field [0001] The invention belongs to the technical field of radar target recognition, and in particular relates to a linear discrimination learning method for extracting one-dimensional range image feature of true and false targets. Background technique [0002] In radar target recognition, the discriminant vector subspace method can increase the difference between heterogeneous target features while reducing the difference between similar target features, so as to extract effective recognition features. Therefore, the discriminant vector subspace method can obtain good recognition performance. [0003] However, the discriminant vector subspace method is only suitable for the case where the sample data is Gaussian distribution, but in practice the sample data distribution may be non-Gaussian distribution, which leads to the decline of the recognition performance of the discriminant vector subspace method. There is room for further improvement in the recognition p...

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Application Information

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IPC IPC(8): G01S7/41
CPCG01S7/41
Inventor 周代英张瑛廖阔冯健
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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