文件列表:

matlab_utilities\assert.m (238, 2007-09-18)
matlab_utilities\chi_square_bound.m (691, 2005-04-28)
matlab_utilities\chi_square_density.m (402, 2005-04-28)
matlab_utilities\chi_square_mass.m (360, 2005-04-28)
matlab_utilities\chi_square_to_gauss.m (678, 2006-01-24)
matlab_utilities\Contents.m (4866, 2007-09-18)
matlab_utilities\covariance_intersection.m (1077, 2007-03-16)
matlab_utilities\demo_bearing_only.m (4888, 2007-04-29)
matlab_utilities\demo_chi_square.m (722, 2006-01-05)
matlab_utilities\demo_ekf_filter.asv (5213, 2009-02-15)
matlab_utilities\demo_ekf_filter.m (5245, 2009-02-16)
matlab_utilities\demo_kmeans.m (531, 2005-11-29)
matlab_utilities\demo_particle_filter.m (4722, 2007-04-29)
matlab_utilities\demo_unscented_filter.asv (4029, 2009-02-09)
matlab_utilities\demo_unscented_filter.m (4058, 2005-04-28)
matlab_utilities\distance_bhattacharyya.m (771, 2007-05-02)
matlab_utilities\distance_KLD.m (929, 2007-05-02)
matlab_utilities\distance_KLD_symmetric.m (736, 2007-05-02)
matlab_utilities\distance_mahalanobis.m (466, 2005-07-29)
matlab_utilities\distance_normalised.m (997, 2006-01-05)
matlab_utilities\dist_sqr.m (736, 2005-10-11)
matlab_utilities\dist_sqr_.m (862, 2005-10-11)
matlab_utilities\dist_sqr_v2.m (812, 2005-04-28)
matlab_utilities\EKF_update.m (2337, 2005-06-09)
matlab_utilities\ellipse_mass.m (540, 2006-02-09)
matlab_utilities\ellipse_sigma.m (506, 2006-02-09)
matlab_utilities\gauss_entropy.m (605, 2007-09-18)
matlab_utilities\gauss_evaluate.m (1134, 2005-10-18)
matlab_utilities\gauss_likelihood.m (1229, 2005-04-28)
matlab_utilities\gauss_power.m (552, 2007-09-18)
matlab_utilities\gauss_regularise.m (363, 2005-04-28)
matlab_utilities\gauss_samples.m (408, 2005-04-28)
matlab_utilities\index_table.m (1505, 2006-06-27)
matlab_utilities\inv_posdef.m (337, 2005-04-28)
matlab_utilities\inv_pseudo.m (406, 2007-06-20)
matlab_utilities\KF_update.m (298, 2005-06-09)
matlab_utilities\KF_update_cholesky.m (673, 2006-07-08)
matlab_utilities\KF_update_IEKF.m (1489, 2009-02-16)
matlab_utilities\KF_update_joseph.m (755, 2006-02-09)
matlab_utilities\KF_update_simple.m (633, 2005-04-28)
... ...

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. Warning: the remainder of this readme is out-of-date. Go to Contents.m and the demo scripts for current information. 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.

近期下载者：

相关文件：

评论：[我要评论] [举报此文件]

收藏者：