针对多传感器的目标识别问题，文中给出并证明了两个传感器 Ｄｅｍ ｐｓｔｅｒ Ｓｈａｆｅｒ （ Ｄ Ｓ）融合识别同一目标时的若干结论及其归纳的结论，同时推出了多（＞ ２）传感器 Ｄｅｍ ｐｓｔｅｒ Ｓｈａｆｅｒ 融合识别同一目标时的递推式，并分析了它们的性质。这些研究是多传感器目标识别系统中不同类传感器的选择及其信息的有效 Ｄ Ｓ融合的理论依据，且融合识别的递推式不仅可减少计算的复杂度，增强多传感器分布识别的可调性，而且可用作多传感器实时融合识别的递推式，这对解决机器人及其军事等领域的目标识别问题有重要价值 Aiming at the multi-sensor target recognition problem, some conclusions and conclusions of the two sensors Dem pster-Shafer (DS) fusion identification and recognition are given and proved. At the same time, multi (> 2) sensor Dem pster-Shafer fusion recursion to identify the same goal, and analyzed their nature. These studies are the theoretical basis for the selection of different kinds of sensors in multisensor target recognition system and the effective DS fusion of their information, and the recursion of fusion recognition can not only reduce the computational complexity and enhance the adjustable recognition of multisensor distribution But also can be used as a recursive method for real-time multi-sensor fusion recognition, which is of great value in solving the problem of target recognition in the robotics and military fields