目前在信息融合领域广泛使用的融合算法是卡尔曼滤波，它在线性高斯模型下能得到最优估计，但在非线性非高斯模型下则无法应用。
The Kalman Filter is widely applied in the Information Fusion at the present, which can get the optimal estimate in the Linear-Gaussian model, but not applied in the nonlinear and non-Gaussian model.
高斯模型与实验半方差变异函数的拟合效果最好，其次是线性模型。
The best fitted theoretical model for the semivariogram is Gaussian model, and then the linear model.
该算法可以直接应用于原系统的非线性模型当中，并且不需考虑系统噪声和量测噪声是否为高斯白噪声，都能得到很好的滤波效果。
It could be directly applied to the nonlinear model of the initial system, and could get good filtering result whether the system noise or measured noise was Gaussian or not.
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