CAJ | 학술논문

针对带相关噪声的多传感器广义系统，提出一种分布式分量标量加权融合稳态降阶Kalman滤波器。应用奇异值分解将原广义系统转化两个等价的降阶子系统，将广义系统状态估计问题转为正常系统的状态估计问题，并求得任两个传感器子系统之间的稳态降阶滤波误差互协方差阵。兼顾融合精度和计算负担，以线性最小方差为融合准则，得到按分量标量加权的稳态Kalman滤波器。该滤波器避免了时刻计算协方差阵和融合权重明显减小了在线计算负担，便于实时应用。 Monte Carlo仿真验证方法的有效性。
침대대상관조성적다전감기엄의계통，제출일충분포식분량표량가권융합은태강계Kalman려파기。응용기이치분해장원엄의계통전화량개등개적강계자계통，장엄의계통상태고계문제전위정상계통적상태고계문제，병구득임량개전감기자계통지간적은태강계려파오차호협방차진。겸고융합정도화계산부담，이선성최소방차위융합준칙，득도안분량표량가권적은태Kalman려파기。해려파기피면료시각계산협방차진화융합권중명현감소료재선계산부담，편우실시응용。 Monte Carlo방진험증방법적유효성。
For generalized systems with multisensory and correlated noise, a distributed fusion steady-state reduced-order Kalman filter is presented. Applying the singular value decomposition, it is transformed into two reduced order coupled subsystems. Then the state estimation problems of generalized systems become the normal systems′ state estimation problem. The cross-covari-ance matrix of steady-state reduced-order filtering errors between any two sensor subsystems is derived. At the same time to consider the fusion accuracy and the computational burden, it proposes the Kalman filter weighted by diagonal matrices in the linear minimum variance sense. The proposed steady-state fusion filter method avoids computing covariance matrices and fusion weights at each time step, so the computational burden can be reduced, and convenient to apply in real time. Simulation example shows the effectiveness of the proposed method.