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一致性战术图象要求数据融合技术把来自不同对象和通常含糊的态势的信息进行组合并传播。信息包含在两种类型的数据中:从传感器测量得到的数值数据,和从操作手以及领域专家得到的语言数据。在实际情况中,数值数据可能是有噪声的、不一致的、不完整的;而语言信息是不确切的、模糊的。为同时处理这两种类型的数据,模糊集和模糊逻辑提供一种方法论,能够为其复杂或不定工程问题及时获得近似的但却一致的战术图像。 本论文介绍了模糊数据融合的功能范例。它包含4个基本组成部分:1)明确因素的模糊化;2)从输入/榆出数字关系和人推导出的模糊知识库;3)基于某种模糊逻辑的模糊推理机构;4)将模糊输出解模糊为明确的输出供工厂使用。对于一个实时的实际系统而言,从给定的明确输入集中给出在线的模糊隶属函数是很重要的。为此,从明确输入数据中估计出最优隶属函数的方法已经实现。这是基于L.A.查德提出的可能性/概率一致论原理。模糊隶属函数和统计输入数据置信度之间的关系已经形成,并用作模糊化的一个设计参数。该技术已应用于二维多传感器多目标跟踪系统。文中还介绍了模糊系统性能评估。利用实验室环境中的仿真数据进行仿真评估使命航空电子技术传感器最佳协同系统MASS。对于使用模糊逻辑技术数据相关函数,显示?
Consistent tactical images require that data fusion techniques combine and disseminate information from different objects and usually ambiguous situations. The information is contained in two types of data: numerical data measured by the sensor, and language data obtained from operators and field experts. In practice, numerical data may be noisy, inconsistent, incomplete; and language information is imprecise and vague. To provide a methodology for the simultaneous processing of these two types of data, fuzzy sets and fuzzy logic, timely and consistent tactical images of complex or indefinable engineering problems can be obtained in time. This paper introduces the functional example of fuzzy data fusion. It consists of four basic components: 1) fuzzification of explicit factors; 2) fuzzy knowledge base derived from input / output relations and people; 3) fuzzy inference mechanism based on some kind of fuzzy logic; 4) The output is defuzzified to a definitive output for use by the factory. For a real-time real system, it is important to give online fuzzy membership functions from a given set of explicit inputs. For this reason, a method of estimating the optimal membership function from explicit input data has been achieved. This is based on the principle of probability / probability consistency proposed by L.A. Chad. The relationship between fuzzy membership functions and statistical input data confidence has been established and used as a design parameter for fuzzification. The technology has been applied to two-dimensional multi-sensor multi-target tracking system. The article also introduced fuzzy system performance evaluation. Simulation Evaluation Using Simulation Data in a Laboratory Environment Mission Avionics Sensors Best Synergy System MASS. For using fuzzy logic technology data related functions, show?