一种针对线性系统Huber估计的正交搜索算法

摘要

对于大数据量、高维参数情况下线性系统Huber估计的计算,常规的非线性估计算法存在计算时间长、收敛速度慢的问题.本文首先根据线性系统Huber估计的特点提出了一种正交搜索算法,然后推导了利用正交搜索法计算Huber估计的方法与步骤,最后通过仿真实验对比正交搜索法与传统最速下降法,结论是正交搜索法在处理数据量大、参数维数高的Huber估计问题具有很强的优势.%Routine nonlinear estimation algorithms have problem of long calculating time and slow convergence speed, when they deal with linear system Huber estimation with great amount of data and high dimension of parameter. Firstly this paper proposes the orthogonal searching algorithm according to the character of Huber estimation, then deduces the algorithm and process to calculate Huber estimation by using orthogonal searching algorithm,finally compares orthogonal searching algorithm with classic furthest falling algorithm through simulation,and the result shows that orthogonal searching algorithm has great advantage to Huber estimation when the amount of data is great and the dimension of parameter is high.