基于相关性函数的多传感器自适应加权融合算法

Multi-Sensor Adaptive Weighted Fusion Algorithm Based on Correlation Function

针对多传感器实际测量中个别传感器出现的数据偏离现象,提出基于相关性函数的自适应加权融合算法。利用相关性函数对数据进行预处理,计算各传感器间的相互支持程度,对于偏离较为明显的数据,用相关性大的数据进行替换;再通过多传感器自适应加权融合算法对数据进行融合。利用该算法对相关数据进行处理,经计算分析得到融合结果为0.999 7,并与传统自适应加权融合算法以及极大似然法的计算结果进行对比。分析结果表明:算法的融合结果更接近实际,融合精度较高。

Aiming at the phenomenon that a sensor may get biased data in the process of multi-sensor measurement,the multi-sensor adaptive weighted fusion algorithm based on correlation function was put forward. The data was preprocessed by correlation function,and then the mutual supportability of each sensor was calculated. In terms of the obvious biased data,they were replaced by high correlation data. Finally,the data was fused by using multi-sensor adaptive weighted fusion algorithm. Therelated data was processed by using the algorithm. The fusion result,which is 0. 999 7,was obtained via calculation and analysis. Moreover,it was compared with traditional adaptive weighted fusion algorithm and maximum likelihood method. The result shows that the fusion result based on the algorithm is close to the real situation and has a higher precision.