Abstract: In order to solve the problem that the Dempster-Shafer Theory（DST） and Dezert-Smarandache Theory（DSmT） both cannot deal with the uncertain information，this paper defines the uncertainty mass in the discernment frame.Before deeply analyzing the Proportional Conflict Redistribution rules（PCR） under the DSmT framework，the PCR2-based Adaptive Universal Proportional Redistribution rule（AUPR） is introduced.And then according to the mathematical model of the sonar，a new group of general basic belief assignment functions（gbbaf） are constructed to deal with the uncertain and imprecise，sometimes even high conflicting information obtained by sonar sensors.At last，the Pioneer 2-DXe mobile robot is used to build the belief distribution map of the environment.The experimental results adequately verify the validity and the practicability of the method proposed in this paper.It supplies a powerful theoretic evidence for dealing with the uncertain information in data fusion theory.

Key words: Dezert-Smarandache Theory（DSmT）, uncertainty mass, redistribution rule, information fusion

摘要： 针对Dempster-Shafer证据理论（DST）及Dezert-Smarandache证据理论（DSmT）均无法处理不确定信息的问题，定义了辨识框架中的不确定因子，通过深入分析比较DSmT框架下的各个冲突分配法则（PCR），提出了一种基于PCR2的自适应通用分配法则（AUPR），并根据声纳的数学模型构造了一组新的声纳信度赋值函数（gbbaf），用以描述声纳获取的不确定和不精确信息，甚至于高冲突信息。最后，以Pioneer 2-DXe机器人为实验平台，绘制了实验场景的各种信度分布图。实验结果充分验证了所提方法的有效性和实用性，为信息融合理论中如何处理不确定信息提供了有力的理论依据。

关键词: 证据理论, 不确定因子, 分配法则, 信息融合