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中文摘要：
研究带白回归滑动平均（ARMA）有色观测噪声的多传感器广义离散随机线性系统，根据Kalman滤波方法和白噪声估计理论，在线性最小方差信息融合准则下，应用奇异值分解和增广状态空间模型，为了提高融合器的精度，提出了按矩阵加权降阶稳态广义Kalman融合器，可统一处理稳态滤波、平滑和预报问题，可减少计算负担和改善局部估计精度。并提出最优加权系数的局部估计误差方差和协方差阵的计算公式。用一个MonteCarlo数值仿真实例说明了所提方法的有效性。
英文摘要：
The multi - sensor generalized discrete stochastic linear system with autoregressive moving average （ARMA） colored observation noises are studied. Based on Kalman filtering method and white noises estimation theo- ry, a reduced order steady - state generalized Kalman fuser weighted by matrices is proposed under the linear mini- mum variance information fusion criterion by using the singular value decomposition and augmented state space mod- el. It can handle the fused filtering, smoothing and prediction problems in a unified framework, and can reduce the computational burden and improve the accuracy of local estimation. The formulas for computing variance and covari- ance matrices among local estimation errors are presented and applied to obtain the optimal weighting coefficient. A Monte Carlo numerical simulation example shows the effectiveness of the proposed method.
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