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One-dimension range profile optimal orthogonal nolinear subspace identification method for radar targets

A radar target and recognition method technology, applied to radio wave measurement systems, instruments, etc., to achieve the effect of improving recognition performance and reducing redundant information

Inactive Publication Date: 2014-03-26
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

The above factors will limit the recognition performance of the regular subspace method based on the kernel function, so there is still room for further improvement in the recognition performance of the regular subspace method based on the kernel function

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  • One-dimension range profile optimal orthogonal nolinear subspace identification method for radar targets
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  • One-dimension range profile optimal orthogonal nolinear subspace identification method for radar targets

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Embodiment Construction

[0026] Specific embodiments of the present invention will be described below in conjunction with the accompanying drawings.

[0027] Optimal Orthogonal Nonlinear Subspace

[0028] Let n-dimensional column vector x ij (i=1,2,...,g; j=1,2,...,N i ) is the j-th one-dimensional range profile of the i-th target, where g is the number of target categories, N i is the number of training samples for the i-th type of target.

[0029] Nonlinear transformation of 1D range image

[0030] the y ij =φ(xi j ) (1)

[0031] Where φ(·) is a nonlinear mapping function. The above formula maps the one-dimensional range image to the high-dimensional feature space, y ij for x ij For the image corresponding to the high-dimensional feature space, its dimension is set to n', which can be arbitrarily large or infinite.

[0032] In a high-dimensional feature space, the inter-class scatter matrix B S and the intra-class scatter matrix W S respectively

[0033] B ...

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Abstract

The invention belongs to the technical field of radar target identification and provides a one-dimension range profile optimal orthogonal nolinear subspace identification method for radar targets. Nonlinear transformation is conducted on a one-dimension range profile of each category of targets, the one-dimension range profile is mapped to high-dimensional linear characteristic space, an optimal orthogonal nolinear transformational matrix is established in the high-dimensional linear characteristic space, characteristic extraction is conducted, a nearest neighbor rule is adopted for classification, and the category of an input target is finally determined. The method comprises the steps of utilizing a kernel function and the one-dimension range profile of the radar target to train a vector to determine matrixes of Ui, Vrj, (K)ij, W alpha and B alpha; determining a vector alpha i (i=1, 2, ..., n) in optimal orthogonal nolinear subspace, determining the transformational matrix A of the optimal orthogonal nolinear subspace, wherein the A ranges from alpha 1 to alpha n; determining a base template vector of the target; determining an optimal orthogonal nolinear projection vector of the one-dimension range profile xt of the input target; determining the Euclidean distance between the optimal orthogonal nolinear projection vector and the base template vector of the target and determining the category of the one-dimension range profile of the input target. The one-dimension range profile optimal orthogonal nolinear subspace identification method for radar targets can effectively improve target identification performance.

Description

technical field [0001] The invention belongs to the technical field of radar target recognition, and relates to a radar target one-dimensional range profile optimal orthogonal nonlinear subspace recognition method. Background technique [0002] The high-resolution radar can obtain the one-dimensional range image information of the target, and the one-dimensional range image reflects the distribution of the target scattering points on the radar line of sight. Compared with the target radar cross-sectional area obtained by the low-resolution radar, the one-dimensional range The image can provide more information about the structure and shape of the target, and this information is very beneficial to the classification of the target. [0003] Subspace-based classification methods are widely used in image recognition, face recognition and other fields, and have also achieved good recognition results in radar target recognition. Among them, the more representative methods are the ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01S7/41
CPCG01S7/411
Inventor 周代英沈晓峰廖阔
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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