多传感器信息融合平滑器算法研究

The Research on Multi-Sensor Information Fusion Smoother Algorithm

为了提高融合估计的精度,采用矩阵加权线性最小方差意义下的最优信息融合准则,对多传感器系统,考虑局部估计误差之间的相关性,给出了最优信息融合固定区间平滑器算法。理论和仿真实验表明,融合平滑器精度在总体上高于各局部子系统的估计精度。与融合预报器相比,融合平滑估计比预测平滑估计精度提高了近5倍,显著地改善了传感器的融合估计精度,并且该平滑器具有容错性。因此,本文提出的融合平滑器算法,对于非实时的信息融合问题具有一定的应用价值。

In order to improve the precision of the fusion estimation, this paper presents the optimal information fusion fix-interval smoother based on the multi-sensor optimal information fusion criterion weighted by matrix in the linear minimum variance sense. The algorithm considers the correlation of estimation errors among local subsystems. The theory and simulation show that the precision of the fusion smoother is higher than that of each local subsystem in general. The algorithm can improve the precision of fusion estimation evidently in contrast of the fusion predictor, and the algorithm has the fault-tolerant. Since that, the algorithm presented in this paper has an applying value for non-real-time information fusion problem.