不受约束的全局最优加权观测融合估计

Unconstrained global optimal weighted measurement fusion estimation

利用矩阵满秩分解方法,基于加权最小二乘理论提出了一种不受各传感器观测阵是否相同、观测噪声是否相关约束限制的加权观测融合估计算法。证明了其估计结果每时刻恒同于集中式融合Kalman估计结果,因而具有全局最优性,且可明显减小计算负担,便于实时应用。通过对GPS目标跟踪系统的两种方案进行仿真说明了它的功能等价性、快速性以及最优性。

A weighted measurement fusion estimation algorithm,not constrained by whether each sensor measurement matrix is the same or not or whether measurement noises are correlated or not,is presented by using full-rank decomposition of matrix and weighted least squares theory.It is proved that the estimation result is equivalent to central fusion Kalman estimation result every moment every time,so it has the global optimality,which can obviously reduce computation burden and is convenient for real time use.Two simulation alternatives for a GPS target tracking system verify its functional equivalence, fastness and optimality.