自校正观测融合解耦Wiener状态预报器

Self-tuning Measurement Fusion Decoupled Wiener State Predictor

对于带未知噪声方差和带不同观测阵的多传感器系统,应用现代时间序列分析方法,基于子系统和加权观测融合系统的滑动平均(MA)新息模型的在线辨识,提出了一类自校正加权观测融合解耦Wiener状态预报器。用动态误差系统分析方法,证明了它按实现收敛于当噪声方差已知时的最优加权观测融合解耦Wiener状态预报器,因而它具有渐近全局最优性。一个目标跟踪系统的仿真例子说明了其有效性。

For the multisensor systems with unknown noise variances, and with diffrernt measurement matrices, using the modern time series analysis method, based on the on-line identification of the moving average （MA） innovation models of the subsystems and weighted measurement fusion system, a class of the self-tuning weighted measurement fusion decoupled Wiener state predictors is presented. By the it is proved that it converges to the optimal weighted measurement fusion dynamic error system analysis method, decoupled Wiener state predictor with known noise variances in a realization, so that it has asymptotic global optimality. A simulation example for a target tracking system shows its effectiveness.