一种复杂环境下的多传感器目标识别系统

对于复杂环境下的多传感器目标识别，其环境因素多变、处理数据量庞大，若采用传统的识别方案，系统的可靠性和抗干扰性不理想。为了解决这些问题，提出了一种用于复杂环境下的神经网络分类器组合结构的多传感器目标识别系统，并通过几种融合方法的仿真实验表明该系统是可行的，能有效地提高系统识别率及鲁棒性。

A Multi-sensor Target Recognition System in the Complex Interference Environment

For multi-sensor target recognition in a complex interference environment, the environment is variational and the data processed is massive. If conventional methods are used, reliability and robustness of the system hardly meet the requirements. To solve these problems, a multi-sensor target recognition system was presented based on the ensemble of neural network classifiers in the complex interference environment. Several fusion methods were simulated, and the results show that the system is practicable and it can effectively enhance the efficiency and robustness of target recognition system.