Radar target intelligent identification method based on task experience migration

Abstract

The invention discloses a radar target intelligent identification method based on task experience migration, and belongs to the field of radar signal processing. The method comprises the following steps: constructing a meta learning identification framework of a multi-class balance center cosine loss basic learner combined with a task loss reciprocal weighting update meta learner, designing a multi-class balance cosine center loss function of a convolutional neural network in the basic learner, and introducing Focal loss to obtain a multi-class balance cosine center loss function; and the problem that the recognition rate of the category targets with fewer sample numbers is reduced due to imbalance of different category sample numbers is avoided. On the basis of a smooth mean absolute error loss function, a task loss reciprocal weighting updating mode of meta-learner parameters is designed, the reference degree of high-relevancy experience is improved, and the problem that the parameter updating process is not converged due to the fact that a meta-training task loss value is too small is solved through small operation. According to the method, the multi-class identification accuracy of the radar target high-resolution range profile under the small sample condition is improved, and the method has popularization and application values.

Description

1. Technical field [0001] The invention belongs to the field of radar signal processing, and in particular relates to a radar target intelligent identification method based on task experience migration. 2. Background technology [0002] High Resolution Range Profile (HRRP) is the projection of the main scattering point of the target in the direction of radar irradiation, which reflects the relative position information of the target scattering point, and reflects the partial structure information of the target to a certain extent. Therefore, it has broad application prospects in the field of target recognition. For cooperative targets, a large number of target HRRP samples can be obtained, but in practical applications, the targets targeted by the radar are often non-cooperative targets, and it is difficult to obtain a sufficient number of non-cooperative target HRRP data. Therefore, HRRP recognition of radar targets under the condition of small samples has become one of th...

AI Technical Summary

Problems solved by technology

[0007] Aiming at the problem of poor HRRP multi-category recognition accuracy of radar targets under the condition of small samples, how to build a suitable meta-learning recognition framework, decouple the characteristics of a single task from the commonality between tasks, and design a suitable basic learner and meta-learner for a single task. Experience transfer of characteristics and commonality between tasks to achieve effective small-sample recognition of radar targets; how to design an appropriate loss function for the construction of basic learners to avoid the recognition rate of class targets with a small number of samples caused by the unbalanced number of samples in multiple categories The problem of descent; for the construction of meta-learners, how to realize the targeted transfer and reference of learning experience for different tasks, to guide new recognition tasks to perform effective small-sample recognition, and to improve the accuracy of small-sample target recognition for new tasks

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