多目标跟踪算法matlab工具包 PHD_CPHD_CBMeMBer.zip

====================[http://ba-tuong.vo-au.com/codes.html]==================== This is a preliminary release for a suite of MATLAB based RFS filtering/tracking codes. The "_common" subdirectory of shared functions should be added to your MATLAB path. The remaining directories contain various filters: "singletarget" Single target filter with RFS observations "bernoulli" Bernoulli filter "phd" PHD filter "cphd" CPHD filter "cbmember" CBMeMBer filter Each filter has different implementation folders: "gms" Gaussian mixture solution for linear Gaussian models "ekf" EKF approximation for non-linear models "ukf" UKF approximation for non-linear models "smc" SMC implementation for nonlinear models For each filter and within each implementation folder, run the "demo" script to see a preconfigured example. e.g. to run the CPHD filter with a UKF based implementation: >> addpath _common >> cd cphd/ukf/ >> demo Notes: 1. All linear Gaussian examples use a 4D CV model (x/y position and velocity) with 2D observations (position only). 2. All non linear examples use a 5D CT model (x/y position, velocity and unknown turn rate) with 2D observations (range and bearing). 3. The demo scripts all use the same basic calling sequence: model= gen_model; %generate model parameters truth= gen_truth(model); %generate ground truths meas= gen_meas(model,truth); %generate measurements est= run_filter(model,meas); %perform filtering handles= plot_results(model,truth,meas,est); %plotting 4. These codes are provided for academic/research purposes. They are designed for readability and demonstration thus they are not optimized for speed. The authors do not provide any guarantees implicit or otherwise regarding these codes. For commercial grade codes please contact the authors directly (ba-tuong.vo@curtin.edu.au, ba-ngu.vo@curtin.edu.au).