Radar target identification method based on label maintaining multitask factor analyzing model

Abstract

The invention discloses a radar target identification method based on a label maintaining multitask factor analyzing model and mainly solves the problem that the prior art is poor in target identification performance under a small sample condition. The radar target identification method includes the steps of firstly, performing normalization and alignment pretreatment on a radar high range resolution profile; secondly, using the preprocessed high range resolution profile to build a label maintaining multitask factor analyzing model; thirdly, performing Gibbs sampling on the parameters of the model, and saving sampling average of the model parameters; fourthly, performing normalization and alignment pretreatment on a to-be-tested sample; fifthly, calculating the frame probability density function value of the to-be-tested sample according to the sampling average, learned by the training steps, of the parameters of the label maintaining multitask factor analyzing model; sixthly, judging the category attribute of the to-be-tested sample according to the frame probability density function value. The radar target identification method has the advantages that supervised learning of the model is achieved, the identification performance under the small sample condition is increased, and the method is applicable to the radar target identification under the small sample condition.

Description

technical field [0001] The invention belongs to the technical field of radar, in particular to a radar target recognition method, which can be used for statistical target recognition of radar high-resolution range profile HRRP. Background technique [0002] The high-resolution range profile HRRP is the superposition of projections of broadband radar target scattering point echoes along the radar line of sight direction, which reflects the distribution of the scattering cross-sectional area of ​​the target scatterer along the radar line of sight direction, including the size of the target and the distribution of scattering points, etc. Rich structural information. As an important feature of radar targets, high-resolution range images are easy to obtain and process, and have become a research hotspot in the field of radar target recognition. [0003] Statistical identification based on Bayesian theorem refers to determining the class belonging of the test sample according to ...

AI Technical Summary

Problems solved by technology

However, the multi-task factor analysis model proposed in this article is the same as the traditional factor analysis model. It is an unsupervised model and does not use the category information of the data itself that is beneficial to classification. Therefore, the recognition performance of the model is still not ideal.

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