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nonlinearfiltertools

于 2007-06-21 发布 文件大小:173KB
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代码说明:

  国外一款非线性估计的工具箱,粒子滤波、UKF、EKF等应有尽有。(abroad estimated Toolbox, the particulate filter, UKF, EKF will not disappoint.)

文件列表:

nftools-v2.0rc4
...............\htm" target=_blank>Changelog
...............\docs
...............\....\QuickGuide.txt
...............\estimators
...............\..........\@dd1
...............\..........\....\dd1.m
...............\..........\....\filtering.m
...............\..........\....\prediction.m
...............\..........\....\private
...............\..........\....\.......\find_cov.m
...............\..........\....\.......\triag.m
...............\..........\....\smoothing.m
...............\..........\@dd2
...............\..........\....\dd2.m
...............\..........\....\filtering.m
...............\..........\....\prediction.m
...............\..........\....\private
...............\..........\....\.......\find_cov.m
...............\..........\....\.......\triag.m
...............\..........\....\smoothing.m
...............\..........\@estimator
...............\..........\..........\display.m
...............\..........\..........\estimate.m
...............\..........\..........\estimator.m
...............\..........\..........\filtering.m
...............\..........\..........\get.m
...............\..........\..........\kalman_gain.m
...............\..........\..........\prediction.m
...............\..........\..........\ricatti.m
...............\..........\..........\riccati.m
...............\..........\..........\set.m
...............\..........\..........\smoothing.m
...............\..........\..........\subsasgn.m
...............\..........\..........\subsref.m
...............\..........\..........\verify.m
...............\..........\@extkalman
...............\..........\..........\extkalman.m
...............\..........\..........\filtering.m
...............\..........\..........\prediction.m
...............\..........\..........\smoothing.m
...............\..........\@gsm
...............\..........\....\filtering.m
...............\..........\....\gsm.m
...............\..........\....\prediction.m
...............\..........\....\private
...............\..........\....\.......\nweights.m
...............\..........\@itekalman
...............\..........\..........\filtering.m
...............\..........\..........\get.m
...............\..........\..........\itekalman.m
...............\..........\..........\set.m
...............\..........\@kalman
...............\..........\.......\filtering.m
...............\..........\.......\kalman.m
...............\..........\.......\prediction.m
...............\..........\.......\smoothing.m
...............\..........\@pf
...............\..........\...\display.m
...............\..........\...\estimate.m
...............\..........\...\filtering.m
...............\..........\...\filtering_init.m
...............\..........\...\normalize.m
...............\..........\...\pf.m
...............\..........\...\prediction.m
...............\..........\...\resampling.m
...............\..........\...\residual.m
...............\..........\...\rndmul.m
...............\..........\@pmf
...............\..........\....\filtering.m
...............\..........\....\pmf.m
...............\..........\....\prediction.m
...............\..........\....\private
...............\..........\....\.......\agd.m
...............\..........\....\.......\cartprod.m
...............\..........\....\.......\defaultParams.m
...............\..........\....\.......\eval_measurement.m
...............\..........\....\.......\expand.m
...............\..........\....\.......\pred_calculation.m
...............\..........\....\subsref.m
...............\..........\@seckalman
...............\..........\..........\filtering.m
...............\..........\..........\prediction.m
...............\..........\..........\seckalman.m
...............\..........\@ukf
...............\..........\....\filtering.m
...............\..........\....\prediction.m
...............\..........\....\private
...............\..........\....\.......\find_cov.m
...............\..........\....\.......\msp.m
...............\..........\....\.......\smsp.m
...............\..........\....\.......\triag.m
...............\..........\....\smoothing.m
...............\..........\....\ukf.m
...............\examples
...............\........\@nfExampleFunction
...............\........\..................\nfdiff.m
...............\........\..................\nfeval.m
...............\........\..................\nfExampleFunction.m
...............\........\..................\nfsecpad.m

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    二维离散小波变换(Mallat快速算法)对图像进行二维离散小波变换, 变换级数大于等于3级,然后进行阈值化处理(阈值约为10左右),再统计系数中0的个数(百分比表示)并进行重构, 最后计算重构图像的峰值信噪比(PSNR). 数据:灰度图像lena.bmp或其它图像, 滤波器系数可以调用matlab中的wfilters函数获得, wfilters函数的使用请在matlab中help wfilters.(Two-dimensional discrete wavelet transform (Mallat fast algorithm) two-dimensional discrete wavelet transform image transformed series is greater than or equal to 3, and then processing thresholding (threshold of about 10 or so), the number of re statistical coefficient 0 ( reconstruct the final calculation of the peak signal-to-noise ratio (PSNR) of the reconstructed image data: the grayscale image lena.bmp or other images, the filter coefficients can call matlab in wfilters function to obtain, wfilters function, expressed as a percentage) in matlab help wfilters.)
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