带不确定噪声方差保性能鲁棒集中式融合Kalman预报器

Guaranteed cost robust centralized fusion Kalman predictor with uncertain noise variances

对于带不确定噪声方差的多传感器系统,基于极大极小鲁棒估计原理,提出保证估计性能的集中式融合鲁棒稳态Kalman预报器.对于预置的估计精度偏差指标,利用Lagrange乘数法求得相应噪声方差的最大扰动域,使该域中所有可容许的噪声扰动,其实际精度对鲁棒精度的偏差被保证在预置范围内,并给出精度偏差的最大下界和最小上界.应用Lyapunov方程方法证明了保证估计性能能够被满足.仿真分析表明了所得结果的正确性和有效性.

A guaranteed cost robust centralized fusion steady-state Kalman predictor is presented for the multisensor system with uncertain noise variances based on the minimax robust estimation principle. A maximal perturbation region of uncertain noise covariances is obtained by using the Lagrange multiplier method. For all admissible perturbations in this region, the deviations of its actual accuracies with respect to the robust accuracy are guaranteed to remain within the prescribed range,and the maximal lower bound and minimal upper bound of accuracy deviations are given. The proof of the guaranteed cost is presented by using the Lyapunov equation approach. A simulation example is given to illustrate the correctness and effectiveness of the proposed method.