带不确定协方差线性相关白噪声系统改进的鲁棒协方差交叉融合稳态Kalman 估值器

对于带有不确定协方差线性相关白噪声的多传感器系统, 利用Lyapunov 方程提出设计协方差交叉(CI) 融合极大极小鲁棒Kalman 估值器(预报器、滤波器、平滑器) 的一种统一方法. 利用保守的局部估值误差互协方差, 提出改进的CI 融合鲁棒稳态Kalman 估值器及其实际估值误差方差最小上界, 克服了用原始CI 融合方法给出的上界具有较大保守性的缺点, 改善了原始CI 融合器鲁棒精度. 跟踪系统的仿真例子验证了所提出方法的正确性和有效性.

Modified robust covariance intersection fusion steady-state Kalman estimators for systems with uncertain-covariance linearly correlated white noises

For multisensor systems with uncertain covariance linearly correlated white noises, an unified approach to design the covariance intersection(CI) fusion minimax robust Kalman estimators(predictor, filter and smoother) is proposed by using the Lyapunov equation approach. A modified CI fusion robust steady-state Kalman estimator and a minimal upper bound of its actual estimation error variances are presented by using the conservative cross-covariances, which overcomes the disadvantage that the upper bound given by the original CI fuser has larger conservativeness, so that the robust accuracy of the original CI fuser is improved. A simulation example of tracking system is given to illustrate the correctness and effectiveness of the proposed method.