kernel density function

基本解释

Using this method, kernel function could be flexibly chosen to estimate sample point's density values according to different locating application scenes.
推广后的定位方法，可根据具体的目标定位场合，灵活选择核函数对样本点进行核密度估计。
By the general assumption that the probability density function of the kernel function can be isolated from separate sub-Gaussian signal or super-signals.
盲源分离算法通常需预先假设源信号的概率密度函数，并由此获得关键的核函数，进而从混合信号中分离出源信号。
The background samples are chosen by thresholding inter-frame differences, and the Gaussian kernel density estimation is used to estimate the probability density function of background intensity.
通过相隔固定的帧差值阅值化得到背景样本值，并采用高斯核密度估计方法计算背景灰度的概率密度函数。