In the multi-sensor target identification problem involving multiple frames, it is important to fuse the potential information characterizing inherent relations among frames with uncertain decision inputs for enhancing the decision-making process. However, due to the influence of environments or other interference factors, the priori knowledge that accurately represents these relations is usually hard to obtain. To overcome this difficulty, we propose a rule mining-based multi-frame decision fusion (abbreviated as RMDF) method, in which the unknown relations can be discovered from a series of historical sensor reports in the framework of belief functions. First, to accommodate data uncertainty, new measures of evidential support and confidence are defined for a constructed multi-frame evidential database, which are generalizations of the support and confidence measures in binary and probabilistic databases. Then, with these measures, an evidential association rule mining algorithm is developed to discover the relations among frames from a series of historical reports. Finally, how these mined rules are properly combined with uncertain decision information using belief function theory is explored. The key benefit of the RMDF method is that it enables modeling the uncertain relations among frames for deriving more accurate decision results. To demonstrate the feasibility and effectiveness of our proposal, an airborne target identification problem is studied under different conditions and the numerical results show that the identification performance of our method is significantly better than the traditional expert knowledge-based method where the available knowledge is inevitably incomplete or inaccurate.

在涉及多个框架的多传感器目标识别问题中，重要的是将潜在的信息（表征框架之间的固有关系）与不确定的决策输入相融合，以增强决策过程。然而，由于环境或其他干扰因素的影响，通常难以获得准确表示这些关系的先验知识。为了克服这一困难，我们提出了一种基于规则挖掘的多帧决策融合（简称RMDF）方法，该方法可以在信念函数框架内从一系列历史传感器报告中发现未知关系。首先，为了适应数据的不确定性，为构建的多框架证据数据库定义了新的证据支持和置信度度量，这是二进制和概率数据库中支持和置信度度量的概括。然后，通过这些措施，开发了证据关联规则挖掘算法，以从一系列历史报告中发现帧之间的关系。最后，探索了如何使用置信函数理论将这些挖掘规则与不确定决策信息正确组合。RMDF方法的主要好处是，它可以对帧之间的不确定关系建模，以得出更准确的决策结果。为了证明我们的建议的可行性和有效性，