【摘要】 对于带有相关观测噪声、未知噪声统计、不同观测阵带有相同右因子的多传感器线性离散定常随机系统,利用相关方法,提出了噪声统计信息的在线辨识器。基于ARMA新息模型,提出了自校正加权观测融合Kalman滤波器,避免了求解Lyapunov和Riccati方程,减少了计算负担,适于实时应用。利用动态误差系统分析(DESA)方法,严格证明了提出的自校正融合滤波器以概率1或按实现收敛于相应的最优融合滤波器,即具有渐近全局最优性。一个3传感器跟踪系统的仿真例子说明其有效性。更多还原

【Abstract】 For the multisensor system with correlated measurement noises,unknown noise statistics and different measurement matrices with identical right factor,by correlated method,the online identifiers of the noise statistics are obtained.Based on ARMA innovation model,a self-tuning weighted measurement fusion Kalman filter is presented,which avoids Lyapunov and Riccati equations,reduces the computational burden and is suitable for real time application.By dynamic error system analysis(DESA) method,it is strictly proved that the proposed self-tuning fused Kalman filter converges to the corresponding optimal fused Kalman filter with probability one or in a realization,i.e.it has asymptotic global optimality.A simulation example for a target tracking systems with 3 sensors shows its effectiveness.更多还原