Kevin Murphy, a postdoc in the MIT AI Lab, provides several MatLab toolboxes, including a Kalman filter toolbox which contains functions and scripts for the Kalman filter, particle filters (in general), and the Unscented Kalman Filter. I share it for other students who are interested in Kalman Filtering.

KalmanAll\Kalman
.........\......\AR_to_SS.m
.........\......\convert_to_lagged_form.m
.........\......\ensure_AR.m
.........\......\eval_AR_perf.m
.........\......\kalman_filter.m
.........\......\kalman_forward_backward.m
.........\......\kalman_smoother.m
.........\......\kalman_update.m
.........\......\learning_demo.m
.........\......\learn_AR.m
.........\......\learn_AR_diagonal.m
.........\......\learn_kalman.m
.........\......\README.txt
.........\......\README.txt~
.........\......\sample_lds.m
.........\......\smooth_update.m
.........\......\SS_to_AR.m
.........\......\testKalman.m
.........\......\tracking_demo.m
.........\KPMstats
.........\........\#histCmpChi2.m#
.........\........\beta_sample.m
.........\........\chisquared_histo.m
.........\........\chisquared_prob.m
.........\........\chisquared_readme.txt
.........\........\chisquared_table.m
.........\........\clg_Mstep.m
.........\........\clg_Mstep_simple.m
.........\........\clg_prob.m
.........\........\condGaussToJoint.m
.........\........\condgaussTrainObserved.m
.........\........\condgauss_sample.m
.........\........\cond_indep_fisher_z.m
.........\........\convertBinaryLabels.m
.........\........\CVS
.........\........\...\Entries
.........\........\...\Entries.Extra
.........\........\...\Entries.Extra.Old
.........\........\...\Entries.Old
.........\........\...\Repository
.........\........\...\Root
.........\........\...\Template
.........\........\cwr_demo.m
.........\........\cwr_em.m
.........\........\cwr_predict.m
.........\........\cwr_prob.m
.........\........\cwr_readme.txt
.........\........\cwr_test.m
.........\........\dirichletpdf.m
.........\........\dirichletrnd.m
.........\........\dirichlet_sample.m
.........\........\distchck.m
.........\........\eigdec.m
.........\........\est_transmat.m
.........\........\fit_paritioned_model_testfn.m
.........\........\fit_partitioned_model.m
.........\........\gamma_sample.m
.........\........\gaussian_prob.m
.........\........\gaussian_sample.m
.........\........\histCmpChi2.m
.........\........\histCmpChi2.m~
.........\........\KLgauss.m
.........\........\linear_regression.m
.........\........\logist2.m
.........\........\logist2Apply.m
.........\........\logist2ApplyRegularized.m
.........\........\logist2Fit.m
.........\........\logist2FitRegularized.m
.........\........\logistK.m
.........\........\logistK_eval.m
.........\........\marginalize_gaussian.m
.........\........\matrix_normal_pdf.m
.........\........\matrix_T_pdf.m
.........\........\mc_stat_distrib.m
.........\........\mixgauss_classifier_apply.m
.........\........\mixgauss_classifier_train.m
.........\........\mixgauss_em.m
.........\........\mixgauss_init.m
.........\........\mixgauss_Mstep.m
.........\........\mixgauss_prob.m
.........\........\mixgauss_prob_test.m
.........\........\mixgauss_sample.m
.........\........\mkPolyFvec.m
.........\........\mk_unit_norm.m
.........\........\multipdf.m
.........\........\multinomial_prob.m
.........\........\multinomial_sample.m
.........\........\multirnd.m
.........\........\normal_coef.m
.........\........\partial_corr_coef.m
.........\........\parzen.m
.........\........\parzenC.c
.........\........\parzenC.dll
.........\........\parzenC.mexglx
.........\........\parzenC_test.m
.........\........\parzen_fit_select_unif.m
.........\........\pca.m
.........\........\README.txt
.........\........\rndcheck.m

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