基于异步多速率观测的噪声去相关融合算法

对于异步多传感器观测数据，基于先同步，再去相关的思想，提出了一种改进的左同步提升异步观测融合算法。采用左同步法避免了右同步过程中的系统状态矩阵求逆和可能出现的非因果问题；对同步后的系统基于Cholesky分解进行噪声去相关处理，理论上分析了去相关处理前后的算法计算量；用信息滤波器进行预测估计，简化了滤波增益的计算过程。仿真结果表明：改进算法能够在不减小跟踪精度的基础上减小计算量，增强了算法的实时性。

Noise decorrelation fusion algorithm based on asynchronous multirate observation

As for asynchronous multisensor observation datas,a modified left synchronously lifting fusion algorithm based on the ideal of synchronism firstly and decorrelation next is proposed. Left synchronism is used to avoid the state matrix inversion and the possibly emerged non-causality problem in the process of right synchronism;decorrelation operation based on Cholesky factorization is conducted to the system after synchronism,the computation cost of the algorithm is analyzed theoretically;information filter is used for prediction and estimation, simplify the computation process of filtering gain. Simulation result shows that,the modified algorithm can reduce computation cost without changing tracking precision and enhance the realtime of the algorithm.