SAR Target Recognition Method Based on Bayesian Multi-kernel Learning Support Vector Machine

Abstract

The invention discloses a SAR target recognition method based on a Bayesian multi-kernel learning support vector machine, which mainly solves the problem of inaccurate recognition of SAR image targets by existing target recognition methods. The implementation steps are: 1) input the original SAR image and preprocess it, and calculate the kernel matrix of different features; 2) combine the kernel matrix according to the multi-kernel learning method; 3) establish a Bayesian multi-kernel for the support vector machine according to the combined kernel matrix Learn the support vector machine model; 4) use the expectation maximization algorithm to solve the Bayesian multi-kernel learning support vector machine model, and obtain the optimal solution; 5) use the optimal solution to perform target recognition on the SAR image test data. The invention effectively combines the inferring ability of the Bayesian method and the distinguishing ability of the multi-core learning method, improves the recognition performance, and can be used for classifying SAR images.

Description

technical field [0001] The invention belongs to the technical field of radar target recognition, in particular to a SAR target recognition method, which can be used for the classification of SAR images. Background technique [0002] Synthetic Aperture Radar (SAR) is an active sensor that uses microwaves for perception. Its imaging is not affected by objective factors such as light and climate, and it can monitor targets all day and all day. High utility value. In addition to the target, the SAR image also contains a large number of clutter, and the SAR image also contains a large number of coherent spots, which makes the detection, identification and recognition of the SAR image very difficult; in addition, due to the different configurations of the SAR target Due to the complexity of the environment, it is impossible to obtain training samples in all situations. Therefore, how to improve the performance of SAR target recognition is an important research direction in radar...

AI Technical Summary

Problems solved by technology

[0009] This kind of classifier that combines single data features with support vector machines is called single-core learning support vector machines. Because different data features have different abilities to represent the similarity and differentiation of data, different data features are selected, and single-core learning The support vector machine shows completely different classification performance. Therefore, the single-core learning support vector machine can only show the characteristics of a certain data feature, and cannot reflect the correlation between the data features, thus affecting the classification performance of the classifier. Make the target recognition rate drop

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