杂波模型为重尾噪声，滤波器为kalman-levy滤波器，此滤波器在噪声不是高斯噪声时效果优于卡尔曼滤波器-Clutter model for heavy-tailed noise filter for the the the kalman-levy filter, this filter is better than the Kalman filter noise is not Gaussian noise

这是用于目标跟踪的粒子滤波代码， 非高斯非线性，其中将扩展卡尔曼滤波与粒子滤波进行比较-The partical filter code is used for target tracking, which is nonlinear and non-gaussian.Andthe extended kalman filtering are used for compared.

包含基于Huber函数和最大相关熵的抗差滤波算法，以及与卡尔曼滤波算法的对比，用于GNSS导航定位粗差处理。同时主函数中包含混合高斯随机数生成方法。(The Huber function and maximum correntropy criterion based robust Kalman filter is included, which can be used for biases in measurements for GNSS positioning. The mixed Gauss

该程序是用混合高斯建模+卡尔曼滤波实现，结果依赖于前景检测效果，结果效果较好，但背景干扰较多。(The program is to use mixed gaussian modeling + kalman filter implementation, the result depends on the foreground detection results, the effect is better, but more background interference.)

针对线性高斯系统，采用共轭分布实现量测噪声未知情况下的状态估计和噪声方差辨识。(For linear Gaussian system, a conjugate distribution is adopted to estimate system state and identificate measurement noise variance.)