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.
图像的小波系数具有很强的非高斯统计特性，可以建立推广的拉普拉斯先验分布，用贝叶斯估计对图像小波系数滤波来达到降噪目的。
The wavelet subband coefficients of images have highly non Gaussian statistics that may be modeled with generalized Laplacian distributions, and Bayesian estimation is used to suppress noise.
粒子滤波算法由于其在非线性、非高斯模型中所表现出的优良性能，使得其越来越受到人们的重视。
Particle filter algorithm has shown its good performance in non-linear and non-Gaussian models and is paid more and more attention.
应用推荐