Reduced dimension weighted measurement fusion Kalman filtering algorithm

摘要：

For the multisensor linear discrete time-invariant systems with correlated measurement noises and with different measurement matrices, based on the linear unbiased minimum variance criterion, a weighted measurement fusion Kalman filtering algorithm is presented. It is identical to that obtained by the Weighted Least Squares(WLS) method, and is numerically identical to the centralized fusion Kalman filtering algorithm, so that it has the global optimality. The optimal weights are given by the Lagrange multiplier method, but its computation burden is large. In order to reduce the computational burden, a reduced dimension weighted measurement fusion Kalman filtering algorithm is derived, which avoids the Lagrange multiplier method, and can significantly reduced the computational burden. The comparison of computational count between two algorithms is given. A simulation example shows effectiveness and correctness of the proposed algorithm.

关键词： Weighted measurement fusion Linear unbiased minimum variance(LUMV) criterion Lagrange multiplier method and Reduced dimension algorithm

作者: Chenjian Ran ZiLi Deng

作者单位: Department of Automation, Heilongjiang University, Harbin 150080

会议类型: 国际会议

会议名称: 2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)

会议地点: 广西桂林

会议语种:英文

页码: 2196-2200

在线出版日期: 2009-06-17（万方平台首次上网日期，不代表论文的发表时间）