Target identification method used for processing fuzzy and high conflict information

Abstract

The invention provides a target identification method used for processing the fuzzy and high conflict information and can reduce computational complexity and improve detected target classification accuracy. The method comprises steps that according to acquired single focal elements under an identification framework, the fuzzy and high conflict information single focal units having the support information are merged in a new combination mode to form new focal elements, and a shape power set formed by the new focal elements is acquired, and the new combination mode refers to merging through utilizing an operator symbol as described in the specifications; according to the information supported by each evidence, the new focal elements in the shape power set and a basic confidence designation function, information synthesis rules based on logic operation are utilized to fuse multiple evidences containing the fuzzy and high conflict information to acquire the preliminary synthesis result for target identification; according to the identification framework and the preliminary synthesis result, detected targets are identified according to preset decision rules. The method relates to the target classification field of the wireless sensor network.

Description

technical field [0001] The invention relates to the field of target classification in wireless sensor networks, in particular to a target recognition method for processing fuzzy and high-conflict information. Background technique [0002] In wireless sensor networks, it is often necessary to detect and classify objects through sensors. The existing target classification methods include decision tree classification method, Bayesian classification method, support vector machine classification method, backpropagation classification method and so on. However, these classification methods are mostly used in fields such as machine learning and data mining that need to process massive data, which not only requires high data volume, but also has high requirements for the storage and computing capabilities of machines. On the contrary, in wireless sensor network applications, due to the limited energy available to sensor nodes, resources such as internal storage capacity, data proce...

AI Technical Summary

Problems solved by technology

[0003] Furthermore, affected by the size, sensor manufacturing process and complex interference factors in the real environment, the information collected by nodes in wireless sensor networks not only has high ambiguity but also often has high conflicts.

If the fuzzy and conflict information collected by each node is discarded, only using the precise information of the node will inevitably lead to inaccurate target detection, or even wrong results, so it is particularly important to effectively deal with fuzzy and high conflict information

Although there are related data fusion algorithms, some of them can’t actually solve conflicting information. Even if some algorithms can solve it, there are still common problems such as excessive calculation, unreasonable generation of focal elements and “explosion of focal elements”

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