matlab_ukf_utilities_鲁棒_惯性天文导航_惯性天文_UKF导航_AUKF

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.