不确定多传感器目标识别的Vague集法

Vague Set Method for Uncertain Multi-sensor Object Recognition

为了解决Vague集理论在多传感器数据融合应用中存在的问题,提出了一种新的不确定多传感器目标识别方法。根据Vague集定义准确地建立了多传感器目标识别系统模型,提出了一种Vague集与优属度相结合的目标识别方法。该方法定义了两Vague集之间的距离,通过求解双目标规划模型客观地确定传感器的权重,避免了传感器权重选取的主观性。利用本文模型可得到各目标的优属度,根据优属度确定最佳目标。仿真实例验证了方法的有效性和具有较高的可信度。

To solve the application problem of Vague set theory in multi-sensor data fusion, a new method for uncertain multi-sensor object recognition was proposed. The model for multi-sensor object recognition system was accurately constructed based on Vague set definition. An object recognition method combined Vague set with optimal membership was presented. It defines the distance between two Vague sets. By solving the bi-objeetive programming model, the weights of sensors can be determined objectively to avoid the subjectivity in selecting sensors＇ weights. This model can be used to obtain the optimal membership of each object. The best object is determined by the optimal membership then. A simulation example shows the method＇s effectiveness and creditability.