多传感器自主在线融合方法

Independent online fusion algorithm for multi-sensor data

在先验知识未知的情形下,针对现有融合算法的不足,提出了一种新的融合算法。为了进一步提高融合精度,算法用均值和自熵两个概念充分挖掘测量中的冗余信息,进而确定传感器的融合权重。此外,为了预防"数据饱和"的发生,算法在迭代过程中引入限定记忆项,保证算法对数据变化的灵敏性。用均值融合算法、冲突证据预处理算法和新算法对样本数据进行仿真。仿真结果表明,运用新算法得到的权值分配方式更加合理,可进一步提高融合精度。

In the case that any prior knowledge was unknown,a new fusion algorithm was proposed in order to deal with the defect of the existing algorithm.In the algorithm,for better fusion accuracy,the mean and entropy based on multi-sensor support degree were used to excavate the redundant information sufficiently,and then the weight coefficient of sensor could be determined.In addition,a limited memory fusion was used to avoid data saturation when the old measurement information was increasing,and ensured delicacy degree when the measurement was changing.To verify the effectiveness of this algorithm,three algorithms were used to detect the sample data.The simulation result shows the weight distribution gained through the new algorithm is more effective,and the accuracy of fusion can be further improved.