基于加权最小二乘法的异质传感器数据融合

Dissimilar-sensor data fusion based on weighted least square

针对异质传感器受限于测量噪声且数据融合结果精度较低的问题,提出了一种基于加权最小二乘法的数据融合新算法。在分析多传感器测量模型的基础上,依据最小二乘原理,推导出多个异质传感器对多个测量参数进行融合估计的加权融合公式,并得出了各传感器加权融合的权系数以及误差矢量的估计方差阵。通过与单传感器及平均估计的测量误差进行比较,证明了应用加权最小二乘数据融合的异质传感器具有较高的测量精度。数值示例与理论推导一致,进一步验证了所提方法的有效性。

To overcome the lower data fusion precision,which is caused by the restriction of noise measurement to dissimilar-sensor,a novel data fusion method based on weighted least square is proposed.The multi-sensor measurement model is analyzed.Then the weighted fusion formula is derived for parameter fusion estimation from multiple dissimilar-sensors,which is based on the principle of least square;and the scaling coefficients of weighted fusion of various sensors and estimated variances of erroneous vectors are obtained.Coparing with the error of single-sensor and mean estimation,the measurement accuracy is higher for dissimilar-sensor data fusion based on weighted least square.Numerical case result is in consistent with theory deduction that further validates the proposed methodology.