【作者】 邓自立； 李春波；

【Author】 DENG Zi-li, LI Chun-bo (Department of Automation, Heilongjiang University, Harbin 150080, China.

【机构】 黑龙江大学自动化系；

【摘要】 针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,基于滑动平均(MA)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平滑器,并证明了它的渐近最优性．与单传感器Kalman平滑器相比,它可提高平滑精度,且算法简单,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．更多还原

【Abstract】 For the multisensor systems with unknown noise statistics, using the modern time series analysis method, based on on-line identification of the moving average (MA) innovation model and 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 smoother weighted by scalars is presented. Its asymptotic optimality is proved, and compared with the single-sensor case, its accuracy is improved. The algorithm is simple, and is suitable for real time applications. A simulation example for a target tracking system shows its effectiveness.更多还原