文件名称:nftools-v2.0
介绍说明－－下载内容均来自于网络，请自行研究使用
非线性滤波工具箱(nft)
包括：
1. Kalman filter
2. Extended Kalman filter
3. Iterated Kalman filter
4. Second order filter
5. Unscented Kalman filter
6. Divided difference filters
7. Central Difference Filter
8. Gaussian sum method
9. Point Mass Method-Toolbox for State Estimation of Nonlinear Discrete Time Stochastic Systems(Nonlinear Filter Toolbox, NFT)
Implemeted estimation techniques
1. Kalman filter
2. Extended Kalman filter
3. Iterated Kalman filter
4. Second order filter
5. Unscented Kalman filter
6. Divided difference filters
7. Central Difference Filter
8. Gaussian sum method
9. Point Mass Method
(系统自动生成,下载前可以参看下载内容)
下载文件列表
nftools-v2.0
............\INSTALL
............\LICENSE
............\docs
............\....\QuickGuide.txt
............\....\NFT-tutorial.pdf
............\templates
............\.........\functions
............\.........\.........\@nfTemplateFunction
............\.........\.........\...................\get.m
............\.........\.........\...................\nfTemplateFunction.m
............\.........\.........\...................\nfeval.m
............\.........\.........\...................\subsref.m
............\.........\.........\...................\nfdiff.m
............\.........\.........\@nfTemplateFunction2
............\.........\.........\....................\nfTemplateFunction2.m
............\.........\.........\....................\nfeval.m
............\.........\.........\....................\nfdiff.m
............\.........\.........\description.txt
............\rmNFToolspath.m
............\addNFToolspath.m
............\TODO
............\estimators
............\..........\@pmf
............\..........\....\pmf.m
............\..........\....\private
............\..........\....\.......\defaultParams.m
............\..........\....\.......\cartprod.m
............\..........\....\.......\expand.m
............\..........\....\.......\eval_measurement.m
............\..........\....\.......\pred_calculation.m
............\..........\....\.......\agd.m
............\..........\....\filtering.m
............\..........\....\prediction.m
............\..........\....\subsref.m
............\..........\@extkalman
............\..........\..........\extkalman.m
............\..........\..........\filtering.m
............\..........\..........\prediction.m
............\..........\..........\smoothing.m
............\..........\@itekalman
............\..........\..........\set.m
............\..........\..........\get.m
............\..........\..........\itekalman.m
............\..........\..........\filtering.m
............\..........\@ukf
............\..........\....\ukf.m
............\..........\....\private
............\..........\....\.......\find_cov.m
............\..........\....\.......\smsp.m
............\..........\....\.......\msp.m
............\..........\....\.......\triag.m
............\..........\....\filtering.m
............\..........\....\prediction.m
............\..........\....\smoothing.m
............\..........\@estimator
............\..........\..........\display.m
............\..........\..........\set.m
............\..........\..........\get.m
............\..........\..........\riccati.m
............\..........\..........\kalman_gain.m
............\..........\..........\estimator.m
............\..........\..........\filtering.m
............\..........\..........\verify.m
............\..........\..........\prediction.m
............\..........\..........\subsasgn.m
............\..........\..........\subsref.m
............\..........\..........\smoothing.m
............\..........\..........\estimate.m
............\..........\@gsm
............\..........\....\private
............\..........\....\.......\nweights.m
............\..........\....\gsm.m
............\..........\....\filtering.m
............\..........\....\prediction.m
............\..........\@dd1
............\..........\....\private
............\..........\....\.......\find_cov.m
............\..........\....\.......\triag.m
............\..........\....\dd1.m
............\..........\....\filtering.m
............\..........\....\prediction.m
............\..........\....\smoothing.m
............\..........\@seckalman
............\..........\..........\filtering.m
............\..........\..........\seckalman.m
............\..........\..........\prediction.m
............\..........\@kalman
............\..........\.......\kalman.m
............\..........\.......\filtering.m
............\..........\.......\prediction.m
............\..........\.......\smoothing.m
............\..........\@dd2
............\..........\....\private
............\..........\....\.......\find_cov.m
............\..........\....\.......\triag.m
............\..........\....\filt