一种多传感器数据自适应空间分级融合算法

Self-adaptive spatial stepped fusion algorithm for multisensor data

当采用分布在不同空间位置上的多传感器观测值对测量噪声干扰下的参数进行融合估计时,被测量的空间分散性对融合结果影响较大。针对该问题,以自适应加权融合算法为基础,提出了自适应空间分级融合算法,并给出了误差分析和应用方法。该算法将融合过程分解为两次寻优,第1次是局部空间的自适应加权寻优,第2次是在全局空间内的融合寻优。计算机仿真结果表明:该算法在估计空间分布不均匀的被测量时优于自适应加权融合算法。

When the survey data sampled by multiple sensors are adopted to estimate the parameter under the interference of measurement noise, the spatial variety of parameter to be measured influence the fusion result remarkably. In order to improve the measurement precision, a self-adaptive weighted spatial stepped fusion algorithm for muhisensor data fusion is presented based on self-adaptive weighted fusion algorithm, as well as error analysis and application method. The fusion process by this algorithm can be divided into partial space selfadaptive weighing optimum seeking and entire space fusion optimum seeking. The simulation results show that this algorithm is better than adaptive weighted fusion algorithm in estimation of spatial variety of measured value.