证据理论和神经网络结合的目标识别方法

提出用证据理论和神经网络结合的高分辨率雷达(HRR)目标识别方法,即首先把多个目标高分辨一维距离像送入学习矢量量化神经网络,进行目标类证据估计;然后用D-S证据理论对各次估计结果进行融合.提出了连续特征空间离散化及类支持度构造的方法,并分析了神经网络识别的误差原因.仿真实验结果表明,这种方法的输出正确识别率比仅仅使用矢量量化神经网络有较大的改善,抗噪能力也有所提高.

Target Recognition Method by Combination of Neural Networks with Evidence Theory

A method based on the combination of neural networks with D S evidence theory was proposed to recognize HRR targets. Multiple HRR images were input into Learning Vector Qualification (LVQ) neural network to estimate target type evidence, the results were fused by D S evidence theory. Methods for feature space discretization and class evidence estimation were proposed. The origin of recognition error of neural network was analyzed. The results of emulation show that the correctness of this method is higher than that of LVQ network method obviously, the ability to counteract disturbance and noise is also raised.