One-dimensional distance multi-classifier fusion recognition method based on class confidence

Abstract

The invention discloses a one-dimensional distance multi-classifier fusion recognition method based on class confidence. The method comprises the steps that K nearest neighbor samples of a preprocessed actual measurement one-dimensional distance image signal sample amplitude feature are extracted by the Euclidean distance measure; the reliability evaluation of the neighborhood sample class is acquired through each sub-classifier classification result to acquire the evaluation matrix of each classifier; and each class confidence of the sample is acquired through the evaluation matrix to realize classification. According to the invention, the method is based on the decision fusion theory; the K-nearest neighbor idea is combined; the nearest neighbor and the Bayesian criterion are used to acquire the class confidence of the target sample to complete target classification; and compared with a single classifier, a traditional voting method and other decision fusion methods, the method has the advantages of high recognition accuracy and great robustness, and is a practical one-dimensional distance image decision fusion recognition method.

Description

technical field [0001] The invention relates to the technology of using multi-classifier fusion to identify one-dimensional distance images, in particular to a one-dimensional distance image multi-classifier fusion recognition method based on category confidence. Background technique [0002] Automatic target recognition is a key area in radar research applications. High-resolution range profile (HRRP) is an important research direction to achieve real-time classification of targets because it contains rich target features and has a small amount of data. Classifier design is an important research direction in HRRP recognition. Designing a reasonable classifier can effectively improve the recognition accuracy and robustness. However, the performance improvement of a single classifier has always been limited. With the idea of ​​fusion, in recent years, thinking about how to synthesize the recognition results of multiple classifiers for the same target to obtain more accurate ...

AI Technical Summary

Problems solved by technology

Traditional fusion methods include majority voting method, weighted average method, and Bayesian method. The relationship between classifiers and their contribution to the classification results, and it only fuses the results of a single test sample under each classifier, resulting in no significant improvement in the fusion result compared with a single classifier, and the Poor stickiness, difficult to apply well in engineering practice

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