Recognition method for finding dynamic target in air in real time

Abstract

The invention relates to a recognition method for finding dynamic target in air in real time, and deeper features can be automatically extracted and learned. According to the technical scheme, target activity rules, aerial target data feature expression, behavior rule discovery and knowledge accumulation based on hierarchical classification are realized, and an aerial target behavior feature structure system platform is constructed; aiming at the target data of which the behavior intention is mastered and target behavior rule analysis, a target behavior rule model which are composed of a data preprocessing module, a track mining module and a track element statistics module can be constructed; the data preprocessing module is used for screening target numbers and cleaning target behavior data, and the track mining module is used for calculating classic tracks and track similarity for each classified track to generate classic track data; and the track element statistics module determines the position and the movement track of a target by means of a sequence image of the target, longitudinally compares the movement of the target, and obtains a saliency detection target of the image in a frequency domain.

Description

technical field [0001] The present invention relates to the field of information processing and analysis, in particular to intelligence big data mining analysis, target feature engineering, and dynamic target prediction methods. Background technique [0002] Knowledge discovery is to extract abstract and valuable information from a large amount of structured data and unstructured data by comprehensively using various learning methods such as statistics, fuzzy learning, machine learning and expert system, and discover potential laws from them. . At present, the analysis of air targets in the space environment is mainly based on the analysis of high-value and small data, and the data is not enough to ensure the comprehensiveness and accuracy of the analysis. For the mining and analysis of multi-objective data, problems such as relatively low depth of utilization levels and insufficient mining of potential connotative laws are more prominent. Faced with a large amount of inte...

AI Technical Summary

Problems solved by technology

However, most of the traditional clustering and clustering models are implemented by spatial clustering algorithms. These methods are simple in principle and easy to implement, but there are also problems such as high error clustering rate. There are dynamic disturbances generated by some drifting clouds, which leads to a decrease in the correct inference rate of subsequent identification problems of high-level plans

Therefore, traditional recognition and tracking algorithms need to manually extract features, and usually need to redesign features as the recognition object changes, and the steps are cumbersome.

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