基于衰减因子的虚拟应答器信息融合方法研究

针对虚拟应答器(VB)信息融合时使用Kalman滤波易出现滤波发散的问题，提出了基于改进Sage-Husa自适应滤波算法的信息融合方法. 首先采用自适应滤波动态调节噪声统计特性参数，抑制滤波发散，在预测误差方差矩阵中引入衰减因子，减小陈旧数据的影响进而提高滤波精度，最后进行仿真实验，将所提出的滤波算法与Kalman滤波和Sage-Husa自适应滤波在VB的位置误差和速度误差上进行对比. 仿真结果证明:在相同的时间内，本文所述算法在VB的定位误差上具有显著优势，具有较好地稳定性. 

Study of virtual balise information fusion method based on attenuation factor

To address the problem that the Kalman filter was prone to filter divergence when using the virtual balise (VB) information fusion, an information fusion method based on the improved Sage-Husa adaptive filtering algorithm was proposed: firstly, the adaptive filtering was used to dynamically adjust the noise statistical characteristics parameters to suppress filter divergence, secondly, an attenuation factor was introduced into the prediction error variance matrix to reduce the influence of stale data and thus improve the filtering accuracy, and finally, simulation experiment was conducted to compare the proposed algorithm with the Kalman filter and Sage-Husa adaptive filter in terms of position and velocity error of the VB. The simulation outcome reveals that the algorithm has an obvious advantage in the positioning error of the VB with better stability in the same time.