实验结果表明，与传统的中值滤波和均值滤波算法相比，该算法能够有效地去除高斯和脉冲噪声，同时能够保留更多的图像细节信息。
As a result, the experiment shows that the algorithm can effectively filter out Gaussian and impulse noises, as well as preserve more detailed information of the original image.
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.
将TVAR模型的信号和反射系数矢量增广为状态矢量后，应用高斯粒子滤波器(GPF)估计TVAR的模型参数，构造了语音增强算法。
When TVAR model signal and reflection coefficients were extended to state vector, Gaussian Particle Filter (GPF) was applied to estimate parameters of TVAR model.
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