A Radar Target Recognition Method Based on Label Consistent Dictionary Learning

Abstract

The invention discloses a radar target recognition method based on label-consistent dictionary learning, which belongs to the field of radar. In particular, the sparse representation of the signal is realized by constructing a complete dictionary with discriminative ability and sparse coding of the sparse signal. The main process of the method: first, construct the LC-KSVD dictionary learning model of high-resolution range images; then initialize the relevant parameters in the model; then use the K-SVD algorithm to solve the optimal solution of the LC-KSVD dictionary learning model; next, Then normalize the dictionary D and the linear classifier matrix W; finally, use D and W to determine the category of the test sample. The method of the invention can make the learned complete dictionary have discrimination ability, not only can better realize the sparse representation of signals, but also 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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