CAJ | 학술논문

针对带不确定模型参数和噪声方差的线性离散多传感器系统，基于极大极小鲁棒估值原理，该文提出一种鲁棒协方差交叉(CI)融合稳态Kalman滤波器。首先，用引入虚拟噪声补偿不确定模型参数，把模型参数和噪声方差两者不确定的多传感器系统转化为仅噪声方差不确定的系统。其次，应用Lyapunov方程证明局部鲁棒Kalman滤波器的鲁棒性，进而保证CI融合Kalman滤波的鲁棒性，且证明了CI融合器的鲁棒精度高于每个局部滤波器的鲁棒精度。最后，给出一个仿真例子来说明如何搜索不确定参数的鲁棒域，并验证所提出的鲁棒Kalman滤波器的优良性能。
침대대불학정모형삼수화조성방차적선성리산다전감기계통，기우겁대겁소로봉고치원리，해문제출일충로봉협방차교차(CI)융합은태Kalman려파기。수선，용인입허의조성보상불학정모형삼수，파모형삼수화조성방차량자불학정적다전감기계통전화위부조성방차불학정적계통。기차，응용Lyapunov방정증명국부로봉Kalman려파기적로봉성，진이보증CI융합Kalman려파적로봉성，차증명료CI융합기적로봉정도고우매개국부려파기적로봉정도。최후，급출일개방진례자래설명여하수색불학정삼수적로봉역，병험증소제출적로봉Kalman려파기적우량성능。
For the linear discrete time multisensor system with uncertain model parameters and noise variances, a Covariance Intersection (CI) fusion robust steady-state Kalman filter based on the minimax robust estimation principle is presented.Firstly, introducing the fictitious noise, the model parameter uncertainty can be compensated, so the multisensory system with both the model parameter and noise variance uncertainties is converted into that with only uncertain noise variances.Secondly, using the Lyapunov equation, the robustness of the local robust Kalman filter is proved, so the robustness of the CI fused Kalman filter is guaranteed and it is proved that the robust accuracy of the CI fuser is higher than that of each local filter. Finally, a simulation example shows that how to search the robust region of uncertain parameters and shows the good performance of the proposed robust Kalman filter.