【摘要】 传统多源信息融合方法大都依赖于事先建立的理论机理模型,一般会引入一定的简化操作。然而实际中的应用往往会较为复杂,建立的理论模型一般存在一定的偏差。在某些情况下,满足性能要求的理论模型甚至无法给出。针对这样的缺陷,该文根据数据驱动的思想,提出了两种基于数据驱动的信息融合实现方法。通过联合利用基于数据的特征集与基于模型的特征集,有效弥补了模型中缺失的信息,从而提高信息融合的性能。将其运用在一个基于声音信息融合的地面车辆辨识实例中,获得了良好的识别性能,展现出将数据驱动处理思路引入信息融合的可行性和优点。更多还原

【Abstract】 Most traditional information fusion methods depend on system models,where certain simplification will be introduced.However,with increased complexity of applications,these models tend to be inadequate and show bias to the real situation.In some cases,precise models are just impossible to build up.Aiming at this problem,two data-driven information fusion methods are presented in this paper.By combining a data-driven feature set with a model-based feature set,the performance of information fusion is improved due to a compensation deficiency for model-based approaches.The proposed method is then applied to acoustic vehicle classification,and better classification performance is achieved,which shows the feasibility and advantages to introduce data-driven ideas into information fusion.更多还原