One-dimensional range profile recognition method based on self-adaptive locality sparsity preserving projection

Abstract

The invention discloses a one-dimensional range profile recognition method based on self-adaptive locality sparsity preserving projection. According to the method, actually measured one-dimensional range profile signal samples are preprocessed; a sparsity coefficient matrix is obtained through sparsity preserving projection (SPP), and a locality similarity matrix is obtained by locality preserving projection (LPP); sparsity preserving projection equations, locality preserving projection equations and a self-adaptive maximum margin criterion are fused, a joint constraint equation set is established, and a self-adaptive locality sparsity preserving projection matrix is obtained; and training samples and test samples are projected into lower-dimensional space through the projection matrix, and a support vector machine is used to carry out training and classification thereof. Based on the sparsity preserving projection, the locality preserving projection and the self-adaptive maximum margin criterion, the method makes full use of sparse reconstruction of the samples and recognition information contained in neighbor relations and combines with the self-adaptive maximum margin criterion to extract lower-dimensional features of the samples, the recognition accuracy of one-dimensional range profile signals is improved, the feature dimensionality is reduced, and the noise immunity is enhanced.

Description

technical field [0001] The invention relates to a one-dimensional range image recognition method based on adaptive local sparseness-preserving projection, in particular to a technology for quickly and accurately identifying a radar target one-dimensional range image in an interference environment, and belongs to the technical field of radar one-dimensional signal recognition. Background technique [0002] Radar automatic target recognition is an important research direction in the field of radar signal processing. With the widespread use of radar automatic target recognition technology, people have higher and higher requirements for radar recognition accuracy, real-time performance and anti-interference performance. The amount of data is large in acquisition, storage and application, and the long processing time becomes a major obstacle in its practical process. Radar high-resolution one-dimensional range profile (HRRP), as a one-dimensional signal, is composed of echoes re...

AI Technical Summary

Problems solved by technology

These methods all use the signal itself or its spatial structure characteristics, and integrate it into the extracted low-dimensional features as identification information. Although they can improve the recognition rate and reduce the feature dimension to a certain extent, they have not deeply explored the connection between signals. relationship, the identification information contained in it is also limited, resulting in limited improvement in recognition rate and weak anti-noise ability, it is difficult to achieve satisfactory recognition results in the actual environment

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