A sparse learning RCS sequence feature extraction method

A feature extraction, sparse technology, applied in the direction of instruments, character and pattern recognition, computer parts, etc., to achieve the effect of improving classification performance and target recognition performance

Inactive Publication Date: 2019-01-18
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Description
  • Claims
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Problems solved by technology

Therefore, there is room for further improvement in the recognition performance of existing traditional projection methods

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  • A sparse learning RCS sequence feature extraction method
  • A sparse learning RCS sequence feature extraction method
  • A sparse learning RCS sequence feature extraction method

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Embodiment Construction

[0025] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the embodiments.

[0026] The present invention improves the feature extraction method in the identification method of the existing projection method to obtain the local structural features in the data distribution, thereby improving the target recognition performance of the identification method of the existing projection method and improving the recognition of real and false radar targets classification performance.

[0027] Its specific implementation process is:

[0028] let x ij (n-dimensional column vector) is the i-th th The j-th class true-false target th training RCS data sequence frames (i.e. training samples), 1≤i≤C, 1≤j≤N i , where N i is the number of training RCS sequence frames of the ith class true and false targets, and N is the total number of training RCS sequence f...

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Abstract

The invention discloses a sparse learning RCS sequence feature extraction method, belonging to the neighborhood of radar target recognition technology. At first, sparse learning is carried out on thetrain sample, Using the sparse coefficient as the adjusting factor of the distance between the features of samples, A sparse learning projection matrix is established, which automatically selects a small number of neighborhood samples through sparse learning, and can better preserve the local structure information of the sample neighborhood, thereby improving the performance of target recognitionand overcoming the shortcoming of the traditional projection method that only global structure features can be extracted.

Description

technical field [0001] The invention belongs to the technical field of radar target recognition, and in particular relates to a sparse learning RCS (target radar cross section) sequence feature extraction method. Background technique [0002] For radar target recognition data, the traditional projection method analyzes the data from a global perspective and extracts the global structural features of the target data distribution. For example, the principal component analysis projection method uses the difference characteristics of the main energy direction of the data distribution to identify the target category, while the discriminant vector projection method uses a transformation to increase the difference between heterogeneous target features and reduce the difference between similar target features. , thereby improving the target recognition rate. However, the traditional projection method ignores the local structural features in the data distribution, and studies have s...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06K9/46
CPCG06V10/462G06F18/217
Inventor 周代英
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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