证据冲突下自适应融合目标识别算法

 利用证据理论对空中目标识别系统的观测信息融合时，Dempster规则对低冲突信息的融合结果较为理想，但无法对高冲突信息有效融合。Dubois &; Prade(DP)规则及证据折扣法可对高冲突信息进行合理融合。为使不同融合方法发挥各自优势，提出一种自适应融合算法。首先将矛盾因子和证据距离两者结合以更全面地表示证据冲突程度，当冲突较小时，选用Dempster规则，反之，根据冲突的具体情况选择使用DP规则或证据折扣法。通过目标识别实验对多种算法进行了对比，表明本文算法既能对高冲突证据进行合理融合，又能使融合结果快速收敛，可以有效地提高识别速度及正确率。

Target Identification by Adaptive Combination of Conflicting Evidence

The observed information in an air target identification system is always uncertain and conflicting. Dempster’s rule can achieve reasonable and specific combination results, but it involves counter-intuitive behaviors when the information high conflicts. Dubois &; Prade (DP) rule and belief functions discounted method can palliate this drawback, but their computation is a bit complex and performance of convergence is not good enough. In order to take advantage of the various rules while avoiding their respective drawbacks, an adaptive combination approach is proposed. The most adapted rule is automatically selected among these rules according to the amount of conflict. Both conflicting beliefs and evidence distance are used to measure the conflict from different aspects. If the conflict is low, Dempster’s rule can be used. Otherwise, DP rule or belief functions discounted method will be selected depending on the details of conflict. An air target identification experiment shows that the adaptive combination approach can get reasonable combination results with good performance of convergence in case of high conflict, which is beneficial to improving the speed and rate of target identification.