卡尔·弗里德里克·高斯就是一个典型，据说他出身在一个体力劳动者家庭，后来却成了现代数学之父。
The model is Karl Friedrich Gauss, supposedly born into a family of manual workers, who grew up to become the father of modern mathematics.
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.
为改善多基地雷达系统对高机动目标的跟踪性能，提出了基于自适应高斯模型和扩展卡尔曼滤波(ekf)的机动目标跟踪算法。
Maneuvering target tracking algorithm based on adaptive Gauss model and EKF was built for improving the tracking performance in Multi-static systems.
在非线性、非高斯条件下进行动基座传递对准，如果采用卡尔曼滤波会出现误差较大甚至发散的问题。
In moving base transfer alignment under nonlinear and non-Gaussian situation, using Kalman Filtering could cause large error or even divergence.
用混合高斯模型得到运动人体的区域，通过卡尔曼滤波对人体进行跟踪，并利用人体的颜色信息进行识别。
Moving areas about human are segmented by using hybrid Gaussian model as background, tracked by Kalman filter, and recognized by using a color-based model.
由于扩展卡尔曼滤波必须假定噪声服从高斯分布，若用于复杂非线性系统，其估计精度不甚理想。粒子滤波对噪声类型没有限制，正在成为非线性系统状态估计的有效近似方法。
Because EKF must assume that the noise is subject to Gaussian distribution, the estimate accuracy is not so good if it is used to estimate the state of complicated nonlinear system.
在毫米波雷达目标跟踪中，角闪烁的非高斯特性将使得经典的卡尔曼滤波器失效。
In MMW radar tracking, the classical Kalman filter will degrade seriously when observation noise is non-Gaussian because of target glint.
在最小二乘法首次描述卡尔弗里德里希·高斯约1794年。
The method of least squares was first described by Carl Friedrich Gauss around 1794.
四十多年前，卡尔曼先生提出了卡尔曼滤波算法，它简单而便于实现，是解决线性高斯环境下的问题的最佳方法。
Kalman filter algorithm, proposed by Mr. Kalman more than 40 years ago, is the best way to solve the problem in the linear Gaussian environment.
四十多年前，卡尔曼先生提出了卡尔曼滤波算法，它简单而便于实现，是解决线性高斯环境下的问题的最佳方法。
Kalman filter algorithm, proposed by Mr. Kalman more than 40 years ago, is the best way to solve the problem in the linear Gaussian environment.
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