自校正标量加权信息融合Kalman滤波器

Self- tuning Information Fusion Kalman Filter Weighted by Scalars

对含未知噪声统计的多传感器系统,用现代时间序列分析方法,基于自回归滑动平均(ARMA)新息模型的在线辨识和求解相关函数矩阵方程组,可在线估计噪声统计,进而在按标量加权线性最小方差最优信息融合准则下,提出了自校正标量加权信息融合Kalman滤波器。它具有渐近最优性,且比每个局部自校正Kalman滤波器精度高,算法简单,便于实时应用。一个目标跟踪系统的仿真例子说明了其有效性。

For the muhisensor systems with unkonwn noise statistics, using the modern time series analysis method, based on on-line identification of the autoregressive moving average （ARMA） innovation model, and based on the solution of the matrix equations for correlation function, the noise statistics can on-line be estimated, and further under the linear minimum variance optimal information fusion criterion weighted by scalars, a self-tuning information fusion Kalman filter weighted by scalars is presented . It has asymptotic optimality, and its accuracy is higher than each local self-tuning Kalman filter. Its algorithm is simple, and is suitable for real time applicatons. A simulation example for a target tracking system shows its effectiveness.