文件列表:

matlab_utilities\Add_relative_Path\addrelpath.m (1298, 2006-08-08)
matlab_utilities\Add_relative_Path\Add_path.m (2716, 2006-08-08)
matlab_utilities\Add_relative_Path\Add_relative_Path.m (943, 2006-08-08)
matlab_utilities\Add_relative_Path\remrelpath.m (1281, 2006-08-08)
matlab_utilities\Add_relative_Path\程序使用说明书.txt (1083, 2006-08-08)
matlab_utilities\Add_relative_Path (0, 2006-11-26)
matlab_utilities\Add_relative_Path.m (1103, 2006-08-11)
matlab_utilities\DataSets\henon.m (395, 2006-08-15)
matlab_utilities\DataSets\Newdataset2.mat (2676, 2006-08-11)
matlab_utilities\DataSets\NEW_dataset.mat (1098, 2006-08-11)
matlab_utilities\DataSets (0, 2006-11-26)
matlab_utilities\EXP_demo_files\demo_ekf_filter_EXP.m (5497, 2006-08-11)
matlab_utilities\EXP_demo_files\demo_particle_filter_EXP2.m (4804, 2006-08-16)
matlab_utilities\EXP_demo_files\demo_unscented_filter_EXP.m (4622, 2006-08-15)
matlab_utilities\EXP_demo_files\particle_filter_Real_EXP.m (5466, 2006-08-16)
matlab_utilities\EXP_demo_files (0, 2006-11-26)
matlab_utilities\gmm_utilities\approximate_gauss_by_gmm.m (862, 2006-01-05)
matlab_utilities\gmm_utilities\approximate_gauss_by_kernels.m (1043, 2006-01-05)
matlab_utilities\gmm_utilities\Contents.m (2897, 2006-01-10)
matlab_utilities\gmm_utilities\covariance_intersect.m (919, 2006-02-06)
matlab_utilities\gmm_utilities\gauss_divide.m (666, 2006-01-05)
matlab_utilities\gmm_utilities\gauss_multiply.m (572, 2006-01-05)
matlab_utilities\gmm_utilities\gmm_addition.m (524, 2005-09-26)
matlab_utilities\gmm_utilities\gmm_conditional.m (708, 2005-12-21)
matlab_utilities\gmm_utilities\gmm_convolve.m (487, 2005-09-26)
matlab_utilities\gmm_utilities\gmm_correlate.m (467, 2005-10-27)
matlab_utilities\gmm_utilities\gmm_counting_algorithm.m (552, 2006-01-04)
matlab_utilities\gmm_utilities\gmm_covariance_intersect.m (2128, 2006-02-13)
matlab_utilities\gmm_utilities\gmm_derivative.m (1505, 2006-01-24)
matlab_utilities\gmm_utilities\gmm_derivative_parameters.m (4172, 2006-01-24)
matlab_utilities\gmm_utilities\gmm_display_1D.m (228, 2006-01-10)
matlab_utilities\gmm_utilities\gmm_display_2D_contour.m (653, 2006-01-19)
matlab_utilities\gmm_utilities\gmm_distance_bayes.m (284, 2005-07-20)
matlab_utilities\gmm_utilities\gmm_distance_bhattacharyya.m (605, 2005-07-19)
matlab_utilities\gmm_utilities\gmm_distance_KLD.m (1718, 2006-01-25)
matlab_utilities\gmm_utilities\gmm_divide.m (896, 2006-01-06)
matlab_utilities\gmm_utilities\gmm_em.m (2456, 2006-01-05)
matlab_utilities\gmm_utilities\gmm_em_auto.m (1477, 2006-01-17)
matlab_utilities\gmm_utilities\gmm_entropy.m (1153, 2006-02-08)
matlab_utilities\gmm_utilities\gmm_evaluate.m (283, 2005-11-15)
... ...

MatLab Utility Functions ------------------------ This collection are m-files I have written over the years that are reusable and I have found very useful in many projects. Some of them are listed below. See Contents.m for a full alphabetic list of functions. Kalman filter ------------- KF_simple_update.m: Basic implementation of KF update. KF_joseph_update.m: Numerically stable KF update (Joseph form). KF_cholesky_update.m: Numerically stable KF update (best). KF_IEKF_update.m: Iterated EKF update. numerical_Jacobian.m: Compute an approximate Jacobian matrix for any non-linear function. chi_square_bound.m: Compute a threshold for an innovation gate that encloses a specified probability mass. inv_posdef: Compute the inverse of a positive-definite matrix Demo filter application: demo_ekf_filter.m Unscented filter ---------------- Note, these implementations are of the basic unscented transform. For more sophisticated versions refer to the recent literature, eg: Julier S.J. and Uhlmann J.K., Unscented Filtering and Nonlinear Estimation, Proceedings of the IEEE, pp 401-422, Volume 92, Number 3, 2004. Two general-purpose functions: unscented_transform.m: Transform a mean and covariance through a non-linear function. unscented_update.m: Perform an unscented Kalman update step with non-linear observe model. Demo filter application: demo_unscented_filter.m Note: The new versions of unscented_transform.m and unscented_update.m require 'vectorised' models. To operate with simple (non-vectorised) models, you may wish to use an old version of the functions. See the 'obsolete' directory and the '_old2' versions. Particle filter --------------- gauss_samples.m: Generate a set of samples from a multi-dimensional Gaussian. gauss_likelihood: Compute the likelihood of a set of innovations. gauss_regularise: Add jitter to particles after resampling according to Gaussian kernel. stratified_random.m: Generate a sorted set of random numbers in range (0,1). stratified_resample.m: Resample step for a particle filter. sample_mean.m: Compute the mean and variance of a set of samples. Demo filter application: demo_particle_filter.m 2-D geometric transforms ------------------------ transform_to_global.m: Convert a local coordinate to the global frame. transform_to_relative.m:Convert a global coordinate to a local frame. pi_to_pi.m: Normalise a polar value to within plus-minus pi. Animation Utilities ------------------- line_plot_conversion.m: Convert an array of line-segments to a single polyline separated by NaNs. sigma_ellipse.m: Create a polyline for an n-sigma covariance ellipse.

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