多功能雷达优先级分配的数据融合方法

W. Komorniczak, J. Pietrasiński, B. Solaiman
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引用次数: 8

摘要

本文研究了多功能雷达资源管理问题。MFR RM包括目标排序和任务调度。研究了基于数据融合的目标排序方法。来自雷达的数据(目标的速度、距离、高度、方向等)、敌我识别系统(敌我识别)和ESM系统(电子支援措施-有关威胁电磁活动的信息)用于确定每个叛逃目标的重要性分配。这个问题也被称为威胁评估。主要问题在于输入信息的多样性。来自雷达的信息属于概率或模糊不完善型,敌我识别信息属于证据型。为了利用这些信息源,一个先进的数据融合系统是必要的。本文对用于威胁评估的模糊系统和神经网络系统进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The data fusion approach to the priority assignment in the multifunction radar
The paper deals with the problem of the multifunction radar resources management (MFR RM). MFR RM consists of target ranking and tasks scheduling. The paper is focused on the data fusion approach to the target ranking. The data from the radar (object's velocity, range, altitude, direction etc.), IFF system (identification friend or foe) and ESM system (electronic support measures - information concerning a threat's electro magnetic activities) are used to decide of the importance assignment for each defected target. The problem is also known as a threat assessment. The main problem consists of the multiplicity of various types of the input information. The information from the radar is of the probabilistic or ambiguous imperfection type and the IFF information is of evidential type. To take advantage of these information sources an advanced data fusion system is necessary. The paper describes a comparison between the fuzzy and the neural network systems which were used in order to perform the threat assessment.
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