自校正加权观测融合Kalman估值器

对于带未知噪声统计的多传感器系统，应用现代时间序列分析方法，基于滑动平均（MA）新息模型参数的两段递推最小二乘法在线辨识，可在线估计未知噪声方差，进而提出了一种加权观测融合自校正Kalman估值器，可统一处理自校正滤波、预报和平滑问题，并证明了它的收敛性，即若MA新息模型参数估计是一致的，则它与相应的最优加权观测融合Kalman估值器的误差收敛到零，因而具有渐近全局最优性。一个带3传感器跟踪系统的仿真例子说明了其有效性。

Self-tuning Weighted Measurement Fusion Kalman Estimator

For the multisensor systems with unknown noise statistics, using the modern time series analysis method, based on on-line identification of the moving average(MA) innovation model parameters by two-stage recursive least squares method, unknown noise variances can on-line be estimated, and a self-tuning weighted measurement fusion Kalman estimator is presented, which can handle the self-tuning fused filtering, prediction, and smoothing problems in a unified framework. Its convergence is proved, i.e. if the parameter estimation of the MA innovation model is consistent, then the error between it and optimal weighted measurement Kalman estimator converges to zero, so that it has asymptotic globally optimality. A simulation example for a tracking system with 3-sensor shows its effectiveness.