基于模糊证据理论的多特征目标融合检测算法

Target Fusion Detection with Multi-Feature Based on Fuzzy Evidence Theory

提出了一种基于平方证据权重的模糊证据组合方法,并应用于弱小目标多特征融合检测算法中,采用了证据理论中的基本概率分配函数来描述判决结果的不确定性,首先提取检测图像的局部灰度均值对比度、局部梯度均值对比度、局部差值和局部熵四个特征,然后对特征进行归一化,再对其进行模糊化并根据先验知识和测量统计的结果对目标各特征值所取空间和待识别目标假设集进行基本概率分配,接着采用自适应加权融合的方法得到目标的基本可信度,最后采用基于博弈概率分布的决策规则得到检测后的目标图像。实验结果表明,该算法能在较大程度上降低目标检测过程中的不确定性,提高系统的检测性能。

A fuzzy evidence combination method based on square evidence weight is proposed,and it is used in the dim target multi-feature fusion detection.The basic probability assignment function of the evidence is used to express decision result＇s uncertainty.First the detection image＇s four features,which are local gray average contrast,local gradient average contrast,local variance and local entropy,are picked up and normalized,then after defuzzification of features,the basic probability assignment of target＇s features with supposed set of recognition target can be got according to a priori knowledge and statistical result.After getting basic credibility using adaptive weighted fusion rule it can get the detected target image by decision making rule of game probability distribution.The experimental results show that the method can reduce the uncertainty during the target detection to a large degree and improve the target detection performance of the whole system.