Fast Algorithms for Multisensor Centralized Measurement Fusion Kalman Filter
Fast Algorithms for Multisensor Centralized Measurement Fusion Kalman Filter
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发表时间：
2005
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通讯作者：
Wu Xiaohui
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作者：
Wu Xiaohui
对于多传感器线性离散时变随机系统，基于Riccati方程的集中式观测融合Kalman滤波算法虽然能给出全局最优状态估计，但其缺点是需要计算高维逆矩阵，计算量大。为了克服这一缺点，利用信息滤波原理，基于修正的Riccati方程、逆预测误差方差矩阵方程、逆滤波误差方差矩阵方程，分别给出了相应的3种全局最优集中观测融合Kalman滤波器的快速算法，避免了高维逆矩阵，可明显降低计算量，并且适合于真实的时间应用。数值模拟实例表明了其有效性。
For the linear discrete time-varying stochastic systems with multisensor, although, the centralized measurement fusion Kalman filter algorithm based on the Riccati equation can give the globally optimal state estimation, but its drawback is to require the computation of high dimensional inverse matrix, which yields a large computational burden. In order overcome this drawback, using the information filtering principle, based on the modified Riccati equation, or inverse prediction error variance mattix equation, or inverse filtering error variance matrix equation, the corresponding three fast algorithms for globally optimal centralized measurement fusion Kalman filter, are presented, which avoid the high dimensional inverse matrix, and can obviously reduce the computational burden, and are suitable for real time applications. A numerical simulation example shows their effectiveness.