Presents a robust estimate for the linear regression of sensor output characteristic based on the robustness of least absolute deviation estimate.
利用最小一乘估计的稳健性，给出了传感器输出特征线性化的特征直线的稳健估计。
Therefore, least absolute deviation, which is more robust than least squares especially for Gaussian noise, is selected to reduce the random error.
绝对偏差最小法是一种适合于存在离群点时的稳健估计算法，可以克服最小二乘法仅在误差为正态分布时才有效的缺点。
Experimental results show that compared with least squares, least absolute deviation can improve location accuracy effectively considering the existence of several outliers.
实验结果表明，与最小二乘法相比，当数据中存在个别误差较大的离群点时，采用最小绝对偏差法可以使得定位精度得到明显改善。
Target trace is fitted by sliding subsection polynomials based on least absolute deviation (LAD) estimation, and the future location of target is predicted with the fitted trace.
采用基于最小偏差估计(LAD)准则的滑动局部多项式拟和目标的运动轨迹，并用拟和的运动轨迹预测目标在下一时刻的位置。
However we want to get the least absolute value of frequency deviation not the least square sum of it.
但是实际需要的是使频率偏差的绝对值最小而不是频率偏差的平方和最小。
However we want to get the least absolute value of frequency deviation not the least square sum of it.
但是实际需要的是使频率偏差的绝对值最小而不是频率偏差的平方和最小。
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