对于带不确定噪声方差的多传感器单通道自回归滑动平均(ARMA)信号系统,当观测噪声中包含白噪声和一个自回归滑动平均(ARMA)有色观测噪声时,通过增广状态方法把ARMA信号系统模型转化为状态空间模型.应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保守系统,提出了鲁棒加权观测融合稳态Kalman信号预报器.对于噪声方差的所有可能的不确定性,它们的实际预报误差方差保证有相应的最小上界.应用Lyapunov方程方法,证明了局部和加权观测融合稳态Kalman信号预报器的鲁棒性和鲁棒精度关系.通过一个仿真例子验证了所提出理论结果的正确性和有效性.

For the multisensor single-channel autoregressive moving average (ARMA) signal system with uncertain noise variances, when the observation noises contain white noises and an autoregressive moving average (ARMA) colored observation noise, ARMA signal system models are transformed into state space models by augmented state method. Using the weighted least squares (WLS) method and minimax robust estimation principle, based on the worst-case conservative system with the conservative upper bounds of noise variances, a robust weighted measurement fusion steady-state Kalman signal predictor is presented. Their actual prediction error variances are guaranteed to have the corresponding minimal upper bounds for all admissible uncertainties of noise variances. Using the Lyapunov equation approach, the robustness and the robust accuracy relations of local and weighted measurement fusion steady-state Kalman signal predictors are proved. A simulation example shows the correctness and effectiveness of the proposed theoretical results.