基于修正倒数型距离贴近度的传感器数据模糊加权融合法

Fuzzy Weighted Fusion Method for Sensor Data Based on Modified Reciprocal Distance Neartude

为提高传感器数据的融合精度和可靠性,提出一种传感器数据模糊加权融合法。对模糊加权融合模型进行研究,得出采用模糊贴近度作为融合权值比隶属度更实用。分析比较5种常用的模糊贴近度方法,其中倒数型距离贴近度分辨率高、计算量最小,但是存在奇异值抑制能力差的问题。针对该问题,建立基于修正倒数型距离贴近度的模糊加权融合模型,以提高算法的可操作性。仿真分析结果说明,与其他模糊贴近度方法相比,采用修正倒数型距离贴近度进行模糊加权融合,具有更高的融合精度和可靠性。

In order to improve the accuracy and reliability of multi-sensor data fusion, a modified reciprocal fuzzy neartude based approach to calculate the weights of the fusion model is proposed. Through the research of the fusion model,it is found that the fuzzy neartude is more practical than fuzzy membership for the calculation of weights. The fusion performance of five types of fuzzy neartude is analyzed,and the reciprocal fuzzy neartude is proved to be one of the best as for the resolution and amount of calculation. However, it can not suppress the singular data well. To address the problem,the reciprocal fuzzy neartude is modified in order to improve the operability of the algorithm. Simulation analysis shows that compared with other fuzzy neartude methods,the modified reciprocal fuzzy neartude based approach can fuse the multi-sensor data with high accuracy and reliability.