Semantic network object identification and judgment method

Abstract

The invention provides a semantic network object identification and judgment method. The semantic network object identification and judgment method aims to provide a comprehensive judgment method capable of lowering calculated amount of evidence compound and update, lowering confliction among evidences with multiple attributes, and improving consistency of identification results. The method is achieved through the following technical scheme that a target semantic database, an evidence receiving module, an evidence semantic knowledge extracting module and an evidence semantic knowledge extension module are established between evidence receiving and evidence output, wherein data interaction is carried out between the target semantic database and the evidence receiving module, the evidence semantic knowledge extracting module and the evidence semantic knowledge extension module, the evidence receiving module receives object identification evidences from sensor identification sources of different types in real time, an evidence semantic knowledge cluster module carries out clustering on elements in multiple attribute collection related to an expanded attribute constraint relation to obtain a plurality of attributive classifications, an evidence compounding and updating module carries out orthogonal calculation and orthogonal compounding and updating on identification evidences and all attributive classification evidences of different levels of attributes from restricted relationship among multiple attributes to obtain judgment results of updated identification evidences.

Description

technical field [0001] The invention relates to a target recognition method based on multi-sensor data fusion in the field of target recognition and tracking pattern recognition, in particular to a comprehensive identification method for multiple identification sources and multiple attributes under the multi-identification framework of an integrated target identification system. Background technique [0002] With the development of target recognition technology, it is a development trend to use multiple types of sensors and multiple recognition techniques to comprehensively identify targets. At present, the target recognition and tracking system based on multi-sensor (radar and infrared) signal fusion, because different types of recognition sources independently recognize the target, the recognition results given are often different at the attribute level, and the recognition results at the same recognition level are often inconsistent. In order to make the integrated recogn...

AI Technical Summary

Problems solved by technology

Although the evidence theory reasoning DS method has been widely used in various data fusion systems, the algorithm implementation has become a difficult problem due to the computational complexity of the Dempster composition rules, the core of the DS method.

Heuristic judgment mainly uses the constraint knowledge between attributes, and updates the reliability of attributes according to certain rules such as scoring, so as to make judgment decisions. Although this method is simple, it does not have a strict mathematical foundation

Moreover, the existing judgment methods do not make good use of the semantic knowledge between the target attributes, and there is no clear mathematical realization of multi-attribute judgment, the reliability is poor, and the rationality of the results cannot be guaranteed.

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