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.
提出并设计了一种基于粒子群优化算法的振动信号的自适应滤波模型。该滤波模型在计算机仿真测试中，获得了很高的效率和良好的结果。
A new adaptive filtering model based on particle swarm optimization (PSO) algorithm is proposed and designed. It is proved to be efficient and effective in the computer simulation example test.
在估计目标状态时，采用了粒子滤波算法，设计了基于自适应表面模型的观测模型；
When estimating the target state, particle filter is adopted, and the observation model is designed based on the adaptive appearance model.
我的主要工作是把粒子滤波算法引人到非线性贝叶斯动态模型中来，对非线性模型进行了模拟。
My main work is applying the particle filter algorithm to random simulate the non-linear Bayesian dynamic models.
粒子滤波技术是近几年出现的一种非线性滤波技术，它适用于非线性系统以及非高斯噪声模型。
The particle filtering is a nonlinear filtering technology, which is suitable for the nonlinear system and non-Gaussian noise model.
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.
并将修正的自适应网格算法和粒子滤波相结合用于跟踪模型范围未知的目标。
The combination of the modified AGMM and PF is also used to tracking target which the model scope is unknown in advance.
在算法的状态估计阶段，采用混合系统粒子滤波和二元估计算法同时估计对象系统故障演化模型混合状态和未知参数的后验分布。
For state estimation of hybrid system with unknown transition probabilities, an adaptive estimation algorithm is proposed based on Monte Carlo particle filtering.
在算法的状态估计阶段，采用混合系统粒子滤波和二元估计算法同时估计对象系统故障演化模型混合状态和未知参数的后验分布。
For state estimation of hybrid system with unknown transition probabilities, an adaptive estimation algorithm is proposed based on Monte Carlo particle filtering.
应用推荐