Reduced Dimension Measurement Fusion Kalman Filtering Algorithm

摘要：

For the multisensor systems with the correlated measurement noises and different measurement matrices, based on the linear unbiased minimum variance (LUMV) criterion, a weighted measurement fusion Kalman filtering algorithm is presented, which is identical to that derived by the weighted least squares (WLS) method, and it 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 their computation burden is large. In order to reduce the computational burden, another reduced dimension algorithm for computing the optimal weights is derived, which avoids the Lagrange multiplier method, and can significantly reduce the computational burden. The comparison of the computational counts between two algorithms for computing weights is given. A simulation example shows the effectiveness and correctness of the proposed algorithm.

关键词： Reduced Dimension Algorithm Weighted Measurement Fusion Linear Unbiased Minimum Variance (LUMV) Criterion Lagrange Multiplier Method Kalman Filtering

作者: Yuan Gao Zili Deng

作者单位: Department of Automation, Heilongjiang University, Harbin 150086, China

会议类型: 国际会议

会议名称: The 22nd China Control and Decision Conference(2010年中国控制与决策会议)

会议地点: 徐州

会议语种:英文

页码: 2184-2188

在线出版日期: 2010-05-26（万方平台首次上网日期，不代表论文的发表时间）