相关观测融合Kalman估值器及其全局最优性

Correlated measurement fusion Kalman estimators and their global optimality

中文关键词  多传感器信息融合  加权观测融合  相关观测噪声  Kalman滤波器  全局最优性

英文关键词  multi-sensor information fusion  weighted measurement fusion  correlated measurement noises  Kalman filter  global optimality

中文摘要

      对于带相关观测噪声和带不同观测阵的多传感器线性离散时变随机控制系统, 用加权最小二乘法(WLS)提出了两种加权观测融合Kalman估值器, 它们包括状态滤波、状态预报和状态平滑. 基于信息滤波器形式下的Kalman滤波器, 证明了在相同初值下, 它们在数值上恒等于相应的集中式观测融合Kalman估值器, 因而具有全局最优性. 但是它们可明显减轻计算负担. 数值仿真例子验证了它们在功能上等价于集中式观测融合Kalman估值器.

英文摘要

      For the multi-sensor linear discrete time-varying stochastic control systems with correlated measurement noises and different measurement matrices, two weighted measurement fusion Kalman estimators are developed by using the weighted least squares (WLS) method. They include the state filtering, state prediction and state smoothing. Based on the Kalman filter in the information filter form, it is proved that under the same initial values, they are numerically identical to the corresponding centralized measurement fusion Kalman estimators, so that they have the global optimality. However, they can obviously reduce the computational burden. A numerical simulation example verifies their functional equivalence to the centralized measurement fusion Kalman estimator.