我们对感知数据融合(SDF)的主要研究目的是:开发一种系统方法,以便设计解释感知信息以及根据这些信息和可用的数据库与知识库推断态势的软件系统。针对该目标,我们开发了一种SDF用的模型-理论框架。该框架的前提是:把数据嵌入数据模型中,把信息处理机构嵌入模型算子中。在本文,我们简要地讨论了模型算子的类型和它们在SDF中的含义。接着介绍融合来自距离和强度传感器的数据的原型SDF系统,并举例说明框架中所介绍的结构。最后,我们说明怎样用框架体系来表示和证明系统特性。SDF框架是通用的,与具体应用领域是无关的。它用于SDF系统的高级说明,并用于检查与任何设计或实现决策无关的相容性。 Our main research on Perception Data Fusion (SDF) is to develop a systematic approach to designing a software system that interprets perceptual information and inferences based on that information and the available databases and repositories. In response to this goal, we have developed a model for SDF - the theoretical framework. The framework of the premise is: data embedded in the data model, the information processing mechanism embedded in the model operator. In this article, we briefly discuss the types of model operators and their implications in SDF. Next, we introduce a prototype SDF system that incorporates data from distance and intensity sensors and illustrates the structure described in the framework. Finally, we show how to use the framework to represent and prove system characteristics. SDF framework is universal, has nothing to do with the specific application areas. It is used for advanced instructions in SDF systems and is used to check compatibility not related to any design or implementation decision.