This code mainly realize the potential of nonlinear environment balanced multi-objective multi-bonuli filter ukf achieve, can effectively solve the problem of multi-target tracking clutter environments.

example10_new-ukf
.................\calc_totalsamp.m
.................\compute_likelihood.m
.................\compute_pD.m
.................\compute_pS.m
.................\compute_qD.m
.................\compute_qS.m
.................\declare_problem.m
.................\ekf_predict_mat.m
.................\ekf_update_mat.m
.................\extract_tracks.m
.................\gaus_merge.m
.................\gen_birthstate_density.m
.................\gen_newstate.m
.................\gen_newstate_fn.m
.................\gen_observation.m
.................\gen_observation_fn.m
.................\kalman_predict_ukf.m
.................\kalman_predict_ukf_sum.m
.................\kalman_update_ukf.m
.................\kalman_update_ukf_sum.m
.................\make_cdnstack.m
.................\memberfilter_ukf.m
.................\mysigfab.m
.................\paperplot_all.m
.................\plotdata.m
.................\plotnums.m
.................\plotoutputdatanl.m
.................\plotoutputnl2.m
.................\plotsim1cmp.m
.................\plotsim1ind.m
.................\plotsim2cmp.m
.................\plotsim2ind.m
.................\plottracks.m
.................\predictcheck.m
.................\predictcheck_smc.m
.................\radarplot_tracks.m
.................\resample.m
.................\rs_norm.mat
.................\rs_unif.mat
.................\siggen_sameX.m
.................\sim1cmp.mat
.................\sim1m_cstats.m
.................\sim1m_mem_smc.m
.................\sim1m_metric.m
.................\sim1m_org_smc.m
.................\sim1mem.mat
.................\sim2m_mem_smc.m
.................\sim2m_metric.m
.................\sim2m_org_smc.m
.................\sim_getcoords.m
.................\sumesf.m
.................\updatecheck.m
.................\updatecheck_smc.m
.................\ut.m

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