An Intelligent Multi-target Comprehensive Recognition Method Based on Data Mining

Abstract

The invention discloses an intelligent multi-target comprehensive identification method based on data mining. The method comprises steps as follows: Step one, acquiring intelligence information; Step two, mining target characteristic knowledge and target association knowledge; Step three, performing intelligent identification on the basis of the target characteristic knowledge; Step four, judging whether targets appear on sites close to each other at almost the same time or not, if yes, executing Step five, and otherwise, executing Step six; Step five, performing intelligent identification on the basis of the target association knowledge; Step fix, feeding back target identification results. The method has the positive effects as follows: the data mining technology is applied to the target identification field, and an automatic and intelligent means is provided for target identification; a target identification knowledge base can be constructed automatically; two target identification thoughts, namely, target characteristic knowledge identification and target association knowledge identification, are put forward, and the multi-target comprehensive identification accuracy can be improved to a large extent.

Description

technical field [0001] The invention relates to an intelligent multi-target comprehensive identification method based on data mining. Background technique [0002] Whether in military or civilian fields, target recognition technology has a wide range of application backgrounds. Therefore, a large number of meaningful theoretical and technical achievements have been accumulated for this technology. Most of the existing target recognition technologies are based on target motion characteristics, Doppler characteristics, and SAR images to classify and identify targets. However, there is still a gap between the existing target recognition technology and the actual application in the aspects of target feature selection, target feature knowledge base construction and target comprehensive recognition. [0003] First of all, in terms of feature selection, generally speaking, the more recognition features, the greater the amount of information, the better the recognition performance,...

AI Technical Summary

Problems solved by technology

At present, the target feature database is mainly established, updated and maintained manually, lacking an automatic and intelligent means

[0005] Finally, for target recognition algorithms, currently most of them use supervised classification methods such as syntactic classification, neural network and fuzzy logic, which have the problems of heavy workload and strong human subjective factors.

[0006] In addition, when there are multiple targets and a variety of intelligence information targeting the same target, there is a lack of effective means for comprehensively identifying targets

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