A Locally Optimal Subspace Recognition Method for One-Dimensional Range Profiles of Radar Targets

Abstract

The invention provides a radar target one-dimensional range profile local optimal sub-space recognition method which effectively improves the performance of recognizing a radar target. According to the method, firstly, a nearest intra-class distance scattering matrix and a nearest between-class distance scattering matrix are calculated through training data; then, a local optimal sub-space is set up according to an optimal ratio criteria, the characteristics of the target are extracted, and the characteristics are classified through a minimum distance classifier; finally, the classification where the input target belongs is determined. The method specifically includes the steps of determining a vector X<W>ij and a vector Xij through a radar target one-dimensional range profile training vector Xij; determining a vector d<W>ij and a vector dij; determining a matrix DW and a matrix DB; determining m vectors a1, a2...and am of the local optimal sub-space; determining the equation (please see the equation in the specification) of the local optimal sub-space according to lambdai and a vector ai, wherein i is 1 or 2 or 3...or m; determining a template library according to the projection of the training vectors in the sub-space A; determining the local optimal sub-image of an input target one-dimensional range profile Xt; determining the distance between the local optimal sub-image and a library template vector, and determining the classification where the input target range profile belongs through the minimum distance classifier.

Description

technical field [0001] The invention belongs to the technical field of radar target recognition, and relates to a local optimal subspace recognition method of a one-dimensional range image of a radar target. Background technique [0002] The subspace method is a classic pattern recognition method, which is widely used in image recognition, face recognition, and has many applications in radar target recognition. The common characteristic subspace method and canonical subspace method have achieved good recognition results in radar target recognition. Among them, the feature subspace can maintain the energy of the original data in the low-dimensional feature space, but it is not optimal in terms of classification performance. Compared with the feature subspace, the regular subspace extracts target features by minimizing the intra-class distance and maximizing the inter-class distance, which improves the performance of target recognition to a certain extent. [0003] However, ...

AI Technical Summary

Problems solved by technology

[0003] However, the canonical subspace method uses the between-class mean and the intra-class mean to describe the intra-class and inter-class distribution macroscopically, but it may not be optimal locally. At the same time, the dimension of the canonical subspace is determined by the number of target categories. Hours can cause loss of classified information

Therefore, there is room for further improvement in the recognition performance of the regularized subspace method

Images