How to use Sequential Monte Carlo method for range-free localization scheme when we estimate the unknown node position in Wireles Sensor Networks?
在估计无线传感器网络未知节点位置时，如何使用与距离无关的贯序蒙特卡罗算法?。
Incorporating intensity distribution and spatial layout, this paper proposes a sequential Monte Carlo probabilistic tracking algorithm using intensity and spatial information.
结合图像的灰度分布和空间布局，提出了一种基于灰度和空间信息的序列蒙特卡罗概率跟踪算法。
Incorporating color distribution and spatial layout, this paper proposes a sequential Monte Carlo filter tracking face algorithm using color and spatial information in HSV color space.
结合图像的色彩分布和空间布局，提出了一种基于HSV色彩和空间信息的序列蒙特卡罗滤波人脸跟踪算法。
Discuss the Sequential Monte Carlo localization method for wireless sensor networks scheme and modify the basic algorithm to overcome the sample degeneracy problem in resampling stage.
讨论了贯序蒙特卡罗方法在无线传感器网络节点定位算法中的实现，并针对再采样阶段的样本缺失现象，对基本算法进行了改进。
Recently, sequential Monte Carlo theory has been applied abroad in different domains such as self-determined navigation, non-linear estimation and finance, and it attracts researchers more and more.
近年来序列蒙特卡罗理论及其应用在自动导航﹑非线性估计与金融等诸多领域受到了越来越广泛的关注。
A single Gaussian distribution is obtained to approximate the posterior distribution of state parameters based on sequential importance sampling and Monte Carlo methods.
通过基于重要性采样和蒙特卡罗模拟方法得到一高斯分布来近似未知状态变量的后验分布。
A single Gaussian distribution is obtained to approximate the posterior distribution of state parameters based on sequential importance sampling and Monte Carlo methods.
通过基于重要性采样和蒙特卡罗模拟方法得到一高斯分布来近似未知状态变量的后验分布。
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