同类多传感器自适应加权估计的数据级融合算法研究

针对同类多传感器测量中含有的噪声,提出了多传感器数据自适应加权融合估计算法,该算法不要求知道传感器测量数据的任何先验知识,依据估计的各传感器的方差的变化,及时调整参与融合的各传感器的权系数,使融合系统的均方误差始终最小,并在理论上证明了该估计算法的线性无偏最小方差性.仿真结果表明了本算法的有效性,其融合结果在精度、容错性方面均优于传统的平均值估计算法.

Study of adaptive weighted estimate algorithm of congeneric multi-sensor data fusion

Aimed at the existence of noise in congeneric multi-sensor measurement,an adaptive weighted estimate algorithm of multi-sensor data fusion was presented.It was unnecessary for this algorithm to be aware of any pre-defined knowledge about the measured data by these sensors,that the algorithm could adjust the fused sensor's weight in time according to the variation in sensor's variance,make the mean square error minimal.It was also proved theoretically that this algorithm is linear and unbiased,in respect of least mean square errors.Simulation results showed that this algorithm is effective and the result of fused data is superior to that of the mean estimate algorithm in respect of accuracy and error-allowableness.