文件列表:

PFPF\Acoustic_Example\AcousticGaussInit.m (924, 2016-06-21)
PFPF\Acoustic_Example\AcousticParticlePlot.m (3641, 2016-07-22)
PFPF\Acoustic_Example\AcousticPropagate.m (315, 2016-02-17)
PFPF\Acoustic_Example\Acoustic_dH_dx.m (1957, 2016-06-14)
PFPF\Acoustic_Example\Acoustic_dh_dxfunc.m (1994, 2016-06-14)
PFPF\Acoustic_Example\Acoustic_example_initialization.m (4329, 2016-07-26)
PFPF\Acoustic_Example\Acoustic_hfunc.m (1542, 2015-09-18)
PFPF\Acoustic_Example\GenerateMeasurements.m (1098, 2016-05-02)
PFPF\Acoustic_Example\GenerateTracks.m (2449, 2016-07-22)
PFPF\Acoustic_Example\GenerateTracks_bounce.m (2372, 2016-02-23)
PFPF\Acoustic_Example\sensorsXY.mat (204, 2015-09-17)
PFPF\ekfukf\cancer\cancer_test.m (1251, 2015-09-17)
PFPF\ekfukf\Contents.m (5717, 2015-09-17)
PFPF\ekfukf\demos\bot_demo\bot_d2h_dx2.m (991, 2015-09-17)
PFPF\ekfukf\demos\bot_demo\bot_demo_all.m (8577, 2015-09-17)
PFPF\ekfukf\demos\bot_demo\bot_dh_dx.m (804, 2015-09-17)
PFPF\ekfukf\demos\bot_demo\bot_h.m (585, 2015-09-17)
PFPF\ekfukf\demos\bot_demo\ekfs_bot_demo.m (8349, 2015-09-17)
PFPF\ekfukf\demos\bot_demo\ukfs_bot_demo.m (7962, 2015-09-17)
PFPF\ekfukf\demos\eimm_demo\botm_demo.m (11904, 2015-09-17)
PFPF\ekfukf\demos\eimm_demo\bot_d2h_dx2.m (991, 2015-09-17)
PFPF\ekfukf\demos\eimm_demo\bot_dh_dx.m (804, 2015-09-17)
PFPF\ekfukf\demos\eimm_demo\bot_h.m (585, 2015-09-17)
PFPF\ekfukf\demos\eimm_demo\ct_demo.m (10187, 2015-09-17)
PFPF\ekfukf\demos\eimm_demo\f_turn.m (924, 2015-09-17)
PFPF\ekfukf\demos\eimm_demo\f_turn_dx.m (1264, 2015-09-17)
PFPF\ekfukf\demos\eimm_demo\f_turn_inv.m (919, 2015-09-17)
PFPF\ekfukf\demos\eimm_demo\trajectory.mat (3778, 2015-09-17)
PFPF\ekfukf\demos\ekf_sine_demo\ekf_sine_d2h_dx2.m (473, 2015-09-17)
PFPF\ekfukf\demos\ekf_sine_demo\ekf_sine_demo.m (6327, 2015-09-17)
PFPF\ekfukf\demos\ekf_sine_demo\ekf_sine_dh_dx.m (420, 2015-09-17)
PFPF\ekfukf\demos\ekf_sine_demo\ekf_sine_f.m (479, 2015-09-17)
PFPF\ekfukf\demos\ekf_sine_demo\ekf_sine_h.m (411, 2015-09-17)
PFPF\ekfukf\demos\imm_demo\imm_demo.m (8117, 2015-09-17)
PFPF\ekfukf\demos\imm_demo\trajectory.mat (3778, 2015-09-17)
PFPF\ekfukf\demos\kf_cwpa_demo\kf_cwpa_demo.m (7431, 2015-09-17)
PFPF\ekfukf\demos\kf_sine_demo\kf_sine_demo.m (2568, 2015-09-17)
PFPF\ekfukf\demos\reentry_demo\make_reentry_data.m (912, 2015-09-17)
PFPF\ekfukf\demos\reentry_demo\reentry_cond.m (757, 2015-09-17)
PFPF\ekfukf\demos\reentry_demo\reentry_demo.m (7408, 2015-09-17)
... ...

% Copyright 2017 Yunpeng Li and Mark Coates % % Licensed under the Apache License, Version 2.0 (the "License"); % you may not use this file except in compliance with the License. % You may obtain a copy of the License at % % http://www.apache.org/licenses/LICENSE-2.0 % % Unless required by applicable law or agreed to in writing, software % distributed under the License is distributed on an "AS IS" BASIS, % WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. % See the License for the specific language governing permissions and % limitations under the License. % % If you make use of this code in preparing results for a paper, please % cite: % % [li2017] Y. Li and M. Coates, "Particle filtering with invertible particle flow", % IEEE Transactions on Signal Processing, 2017. % % % If you have any questions to this code, please contact Yunpeng Li at % yunpeng.li@mail.mcgill.ca % % This code is tested on Matlab R2015b. % Date: 07/24/2016 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % This code requires the following packages (in addition to standard % Matlab toolboxes) % % EKF/UKF toolbox: % % http://becs.aalto.fi/en/research/bayes/ekfukf/ % % This code also incorporates the code published at % http://pagesperso.telecom-lille.fr/septier/MatlabCode_Septier_SMCMC.zip % to evaluate the SmHMC algorithm proposed in % [Septier16] F. Septier and G. W. Peters, “Langevin and Hamiltonian based sequential % MCMC for efficient Bayesian filtering in high-dimensional spaces,” % IEEE J. Sel. Topics Signal Process., vol. 10, no. 2, pp. 312–327, Mar. 2016. % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % The main folder provides the following primary functions % 1. run(alg_executed) % This is the main function of the code. % It starts by initializating the simulation and % algorithm parameters, then runs each filter with % the required number of random trials. % The filter outputs are saved in a mat file. % alg_executed is a cell that can include the following algorithms. % PFPF_LEDH: the PFPF (LEDH) proposed in [li2016] % PFPF_EDH: the PFPF (EDH) proposed in [li2016] % LEDH: the localized Daum and Huang filter (LEDH) % EDH: the exact Daum and Huang filter (EDH) % GPFIS: the Gaussian particle flow particle filter (GPFIS) % SmHMC: the Sequential Markov chain Monte Carlo based on the % Manifold Hamiltonian Monte Carlo kernel (SmHMC). % Note that it can be only applied to the Septier16 example, % as it requires the target distribution to be log-concave. % EKF: the extended Kalman filter (EKF) % BPF: the bootstrap particle filter (BPF) % % Two simulation setups are included and can be specified in initializePS.m: % 'Acoustic', is the acoustic tracking example, % 'Septier16' is the large sensor field tracking example reported in [Septier16]. % See Section V of [Li2016] for more details. % % 2. plotErrors(file_name) % This function calculates filtering errors and display them. % file_name is a string that contains the name of the mat file that stores filter outputs. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

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