“低慢小”目标自动识别技术是低空和超低空突防一个亟待解决的重要问题。相对于常规目标识别，“低慢小”目标识别数据更加不精确、更加不完整、更加不可靠、系统不确定性大，导致在对多传感器给出的信息进行融合时，往往会出现大量高冲突信息，如何解决这种高冲突信息有效融合，已成为“低慢小”目标识别亟待解决的关键技术问题。本项目将同时考虑目标识别准确性和实时性，针对“低慢小”目标识别中不确定信息处理问题，将融合识别技术引入“低慢小”目标识别问题，采用极限学习ELM与D-S证据理论相结合的方法对问题展开深入研究。引入仿生学算法ELM对“低慢小”目标数据进行证据体构建，摸索融合识别过程产生的悖论规律，寻找合理的冲突证据评价指标，解决冲突证据融合问题，提出有效的冲突证据快速融合方法，降低系统不确定性，提高系统决策能力。本项目开展及其预期成果，可为“低慢小”目标识别提供必要的技术支撑，具有重要的军事及应用价值。

Low Slow Small (LSS)target auto recognition technique is an imperative problem to low and very low-altitude defense penetration. Compared to normal target recognition, the data of LSS is more inaccuracy, imperfect and unreliable that the uncertainty of recognition system is heavy, a great many high conflict information has to be dealt, how to fuse these information has become a key technique of LSS target recognition system. In order to conquer the uncertainty of the system, we give consideration to both the accuracy and speedy of the system, the fusion recognition technique which combine extreme learning machine(ELM) with D-S theory method is adopted. ELM is a bionics algorithm, and we use it to the build evidence body of LSS target recognition system, we attempt to find the law of the paradoxes that generated by fusion course and good evaluating indicator of conflict system. The decision ability will be enhanced and the uncertainty will be decreased by the proposed effective conflict evidence fusion schemes. The work of this project will provide necessary theoretical foundations and technical supports to LSS target recognition system, this is of significant military meanings and application values.