Research On Data Modeling And Information Processing Of Multi-source Detection

A great advantage of submarine is concealment. It’s an important weaponry in modernnaval battle. Detecting, recognizing and locating submarines timely and effectively is thekey to win the battle. The main content of this thesis is about data modeling andinformation processing of multi-source detection, including non-acoustic anti-submarinedetecting, information processing based on complex network theory, data analysis and theimplementation of simulation demo system.Three kinds of airborne non-acoustic detector models are designed in order to simulatemulti-source detection data, including thermal wake detector model, laser radar detectormodel and magnetic anomaly detector model. Then the multi-source detection informationis processed by federated Kalman filter to get single detection result. Based on ontologytechnology, information ontology model of detection information entities is built.According to the property of information entities, information ontology model is abstractedinto a complex network that consists of information entities. By clustering method, theinformation entities clustering analysis is achieved, which implements high level datafusion of anti-submarine detection and improves the confidence level. At last, a detectionprocessing demo system is designed based on Java language and the system shows thesoftware framework and the experiment result proves the effectiveness of anti-submarinemodeling and information processing.This thesis has shown that these three kinds of airborne non-acoustic detector modelscan effectively detect submarine voyage data. The data fusion based on complex networktheory improves the confidence level of detection results, which provides a new method formulti-source detection information processing...