基于DS证据的信息融合算法多指标融合

在雷达跟踪中，算法方案的综合评价可通过DS证据理论进行多属性融合。而在实际应用中存在部分指标缺失等问题，导致证据间出现高冲突，使得原始DS证据合成方法失效。在分析了多指标融合中由于3类信息不完整而导致的高冲突证据后，提出了将算法方案的综合评价作为群体证据，把单个证据与群体证据之间的模糊距离作为证据融合权重，基于权重改进DS证据合成方法，解决了高冲突证据下的多属性融合。实验结果表明基于群体证据模糊距离的DS证据合成方法可以较好地解决雷达跟踪中算法方案综合评价问题。

A DS-Based Multi-index Fusion of Information Fusion Algorithm

The evaluation of algorithm scheme in radar tracking may be achieved by multi-attributes fusion via DS theory. However, the missing of some index in practice will result in high conflicts among evidences and make the results of classical DS-based fusion methods paradox. By analyzing three kinds of incomplete tracking information, this paper proposes a new DS-based fusion method, in which the evaluation of algorithm scheme is treated as the group evidence and the fuzzy distance between a single evidence and group evidence is adopted as the weight in DS combination. Experiment results show that the improved DS-based fusion could resolve the problem of evidence portfolios in radar tracking.