Sparse representation sample distribution boundary preserving feature extraction method

Abstract

The invention discloses a sparse representation sample distribution boundary preserving feature extraction method, and belongs to the technical field of radar target recognition. According to the method, boundary points and corresponding weight coefficients of sample distribution regions are determined through sparse representation, an objective function representing separation gaps of heterogeneous sample distribution regions is established to obtain a sparse representation sample distribution boundary preserving transformation matrix, and the separation degree between heterogeneous feature local regions can be increased through transformation; and finally, the RCS data sequence frame number to be subjected to feature extraction is projected based on the obtained sample distribution boundary preserving transformation matrix to obtain a projection feature vector of the object to be extracted, so that the radar target recognition performance is improved when radar target recognition processing is carried out based on the projection feature vector extracted by the invention.

Description

technical field [0001] The invention belongs to the technical field of radar target recognition, in particular to a sparse representation sample distribution boundary-preserving feature extraction method for radar target recognition. Background technique [0002] Radar target recognition needs to extract the relevant information signs and stable features (target features) of the target from the radar echo of the target and determine its attributes. It identifies targets based on their electromagnetic backscatter. Using the characteristics of the scattered field generated by the target in the far zone of the radar, information for target identification (target information) can be obtained. The acquired target information is processed by computer and compared with the characteristics of existing targets, so as to achieve the purpose of automatic target identification. Radar target recognition includes two parts: feature extraction and classification recognition. [0003] Fe...

AI Technical Summary

Problems solved by technology

However, the local structure preservation method does not consider the degree of separation between different types of feature regions, which may cause certain aliasing between the boundaries of different types of sample regions, which limits the further improvement of recognition performance.

Therefore, there is room for further improvement in the recognition performance of existing local structure preserving methods

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