Neighborhood characteristic space discriminant analysis based radar target identification method

Abstract

The invention discloses a neighborhood characteristic space discriminant analysis based radar target identification method. Each class of radar target data is divided into training samples and testing sample; the within-class neighboring characteristic spaces and inter-class neighboring characteristic spaces of the training samples are established, and vertical vectors from sample points to the within-class neighboring characteristic spaces and the inter-class neighboring characteristic spaces and weighted values of the vertical vectors are calculated; within-class scattering matrixes and inter-class scattering matrixes of all the training samples are established, transformational matrixes from a high-dimensional radar target data space to low-dimensional characteristic sub-spaces are solved, all the training samples and testing samples are transformed from the high-dimensional radar target data space to the characteristic points in the low-dimensional characteristic sub-spaces according to the obtained transformational matrixes to complete characteristic extraction; a nearest neighbor method is adopted to classify the characteristic points of the testing samples to complete radar target identification. The method can effectively improve the learning capability of the sub-spaces, improve the radar target identification performance under the conditions of limited training samples and is low in computation burden.

Description

technical field [0001] The invention belongs to the technical field of radar data processing, and in particular relates to a radar target recognition method based on neighborhood feature space discriminant analysis. Background technique [0002] The subspace learning method has been widely used in radar target recognition. Its classic representatives include Principal Component Analysis (PCA) and Linear Discriminant Analysis (Linear Discriminant Analysis, LDA). Their common feature is the use of point-to-point distance measure. When the number of training samples is sufficient, the above two algorithms can get better recognition results. However, when the number of training samples is very limited, their subspaces are likely to fail to truly represent the intrinsic geometry of the original sample space, leading to a degradation in radar target recognition performance. [0003] In order to enhance the representation ability of limited training samples, some scholars have pr...

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Problems solved by technology

[0005] In view of this, the present invention aims at the problem that the performance of radar target recognition is degraded due to interpolation errors and subspaces that cannot truly reflect the geometric structure relationship between samples under the condition of limited training samples, and provides a method based on neighborhood features Radar Target Recognition Method Based on Spatial Discrimination Analysis

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