摘要
针对目前多传感器系统中常用的信息融合方法,识别率较低、网络稳定性不好、不能很好地处理不确定性等问题,提出一种基于神经网络和DS方法的信息融合算法。该方法兼顾神经网络和DS推理二者的优势,有效地解决了目前信息融合方法对大噪声不确定性传感器测量信息的误识别问题。仿真实验结果验证了该算法在提高目标识别率和抗噪能力方面的有效性。
A new algorithm of data fusion based on neural networks with DS evidential theory is presented to these questions of low accurate identification,bad stabilization and solution of uncertainty in some ways of multi-sensor system at present.This method has the advantage of both neural and DS evidential theory and solves the problem that the general ways of data fusion can not identify the multi-sensor＇s uncertainty information of great noise at present.The simulation shows that the way can effectively the rate of the targets＇ identification and great antinoise capacity.
出处
《计算机工程与应用》
CSCD
北大核心
2006年第1期174-176,179,共4页
Computer Engineering and Applications
基金
国家863高技术研究发展计划资助项目(编号:2002AA731215)
