搜索资源列表
particle-filter-mcmc
- 该程序为基于粒子滤波的一种新算法,综合MCMC Bayesian Model Selection即MONTE CARLO马尔克夫链的算法,用来实现目标跟踪,多目标跟踪,及视频目标跟踪及定位等,解决非线性问题的能力比卡尔曼滤波,EKF,UKF好多了,是我珍藏的好东西,现拿出来与大家共享,舍不得孩子套不着狼,希望大家相互支持,共同促进.-the program based on particle filter for a new algorithm, Integrated Bayesian MCMC
Adaptive-Online-Learning
- 基于EKF的神经网络自适应在线学习算法，包含例子和文档。-We show that a hierarchical Bayesian modeling approach allows us to perform regularization in sequential learning. We identify three inference levels within this hierarchy: model selection, parameter estimation, and
UKF
- 基于非线性动力系统的无迹卡尔曼滤波matlab程序-onlinear state estimation is a challenge problem. The well-known Kalman Filter is only suitable for linear systems. The Extended Kalman Filter (EKF) has become a standarded formulation for nonlinear state estimation.
ekf2
- 一种快速Kalman滤波算法实现,。对于某些不能够采取离线计算的滤波过程来说，它可以在保证一定精度的同时极大地提高计算速度和减少计算占用资源- EKF Extended Kalman Filter for nonlinear dynamic systems [x, P] = ekf(f,x,P,h,z,Q,R) returns state estimate, x and state covariance, P for nonlinear dynamic system:
demo3
- 在demo中，用EKF和有噪声的EKF训练非线性、非平稳数据。-In this demo, I use the EKF and EKF with noise adaptation to train a neural network with data generated a nonlinear, non-stationary state space model. Adaptation is done by matching the innovations ensemble covariance