Radar target recognition method based on tag consistent dictionary learning

Abstract

The present invention discloses a radar target recognition method based on tag consistent dictionary learning, and belongs to the field of radar. Sparse representation of signals is realized by constructing a complete dictionary with a discriminating ability and sparse coding for sparse signals. The method comprises the main flows of: constructing a LC-KSVD dictionary learning model of a high-resolution range image; initializing relevant parameters in the model; using a K-SVD algorithm to solve the optimal solution of the LC-KSVD dictionary learning model; normalizing the dictionary D and thelinear classifier matrix W again; and finally, according to the D and the W, determining the category of the test sample. According to the method disclosed by the present invention, the learned complete dictionary has the discriminating ability, so that not only the sparse representation of the signals can be better realized, but also the method can be used for classification.

Description

technical field [0001] The invention belongs to the field of radar, and in particular realizes the sparse representation of the signal by constructing a complete dictionary with discriminative ability and sparse coding of the sparse signal. Background technique [0002] The radar high-resolution range profile (HRRP) is the vector sum of the projected echoes of the scattered points of the target obtained by using the broadband radar signal in the direction of the radar line of sight. It contains many important structural information such as target size and scattering point distribution, and is easy to acquire, store and process, so it is widely used in the field of radar automatic target recognition. [0003] For the acquisition of radar HRRP signals, it is usually necessary to use a sampling rate higher than the effective dimension of its physical process to sample information. In this way, the dimensionality of the data will inevitably increase, and the redundant dimension...

AI Technical Summary

Problems solved by technology

However, the K-SVD algorithm model is to obtain the sparse representation of the signal under the constraint of the minimum sparse representation reconstruction error, and the complete dictionary constructed does not have the ability to identify, and the recognition rate of this method is not ideal due to this constraint.

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