Abstract: Aimed at the recognition problem of multi-targets with multiple characteristic indexes，a new fusion method for the multi-sensor data is proposed.The method uses the max-min membership function to obtain the index membership matrix.By solving the optimal programming of maximizing the total absolute deviation of the comprehensive membership for all target types，the weights of attributes are derived.Hence，the algorithm of object recognition is given.The method may improve the objectivity and distinguishing degree of target recognition.The example of parts recognition proves that the method is both effective and exercisable.

Key words: multi-sensor, data fusion, object recognition, absolute deviation

摘要： 针对具有多个特征指标的多目标识别问题，提出了一种新的多传感器信息融合方法。该方法根据最大最小隶属度函数得到指标隶属度矩阵，通过求解各目标类别综合隶属度的绝对偏差最大的优化问题，客观地获得了属性的权重，从而给出目标识别算法，提高了识别结果的客观性和区分程度。工件识别实例验证了算法的有效性和实用性。

关键词: 多传感器, 数据融合, 目标识别, 绝对偏差