不确定多传感器系统鲁棒观测融合Kalman 预报器

对于带不确定模型参数和噪声方差的线性离散时不变多传感器系统, 用虚拟噪声补偿不确定参数, 系统转化为仅带噪声方差不确定性的多传感器系统. 用加权最小二乘法和极大极小鲁棒估计准则, 基于带噪声方差保守上界的最坏情形保守系统, 提出一种鲁棒加权观测融合稳态Kalman 预报器, 并应用Lyapunov 方程方法证明了它的鲁棒性, 同时给出了与鲁棒局部和集中式融合Kalman 预报器的精度比较. 最后通过一个仿真例子说明了如何搜索参数扰动的鲁棒域, 并验证了所提出的理论结果的正确性和有效性.

Robust measurement fusion Kalman predictor for uncertain multisensor system

For the linear discrete time-invariant multisensor system with uncertain model parameters and noise variances, by using the approach of compensating the parameter uncertainties by a fictitious noise, the system is converted into a system with uncertain noise variances only. By using the weighted least squares(WLS) method and the minimax robust estimation principle, based on the worst-case conservative system with the conservative upper bounds of noise variances, a robust weighted measurement fusion Kalman predictor is presented, and its robustness is proved by using the Lyapunov equation approach. The accuracy comparisons among the robust local Kalman predictors, weighted measurement fusion Kalman predictor and centralized fusion Kalman predictor are given. A simulation example is presented to demonstrate how to search the robust region of uncertain parameters and to show the effectiveness and correctness of the proposed results.