A multi-frame heterogeneous data fusion recognition method based on trust

Abstract

The invention discloses a multi-frame heterogeneous data fusion recognition method based on trust, in which a corresponding classifier is trained in the feature space of each recognition frame, and each classifier is used to classify a plurality of known training samples, Obtain the classification result of each known training sample, and calculate the conversion relationship between each recognition frame and the target recognition frame according to the corresponding relationship between the classification result of each known training sample and its real classification result; The relationship transforms the classification results of target samples in different identification frameworks into the target identification framework and fuses them to obtain the final classification results of target samples; the present invention combines the classification results obtained by different classifiers (located in two different identification frameworks) Transform to the same identification framework, and use the evidence theory to fuse and estimate the conversion rules, and finally realize the fusion decision of different framework data, which can improve the accuracy of heterogeneous data identification.

Description

【Technical field】 [0001] The invention belongs to the technical field of target recognition, in particular to a trust-based multi-frame heterogeneous data fusion recognition method. 【Background technique】 [0002] The recognition of complex pattern systems based on classifier fusion tasks is an important and challenging field of current research, and one of the key issues is how to obtain more available knowledge and improve classification accuracy, especially in knowledge-unknown and complex patterns. in the classification system. The idea of ​​classifier fusion is that different classifiers can provide (more or less) complementary information to achieve higher classification accuracy. In the classifier fusion technology, the identification framework must be unified first, and two classification information under the same identification framework can be fused to increase their context information. However, due to the different object recognition frameworks observed by dif...

AI Technical Summary

Problems solved by technology

In reality, due to differences in the identification frameworks of different sensors, the relationship between different identification frameworks is unknown and non-linear. Therefore, it is difficult to achieve multi-frame heterogeneous data fusion recognition.

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