资 源 简 介
This is an implementation of a gaussian mixture probability hypothesis density filter (GM-PHD) for a simulated tracking problem. The problem specification is given in the above paper - in summary, two targets move through the environment, there is a lot of clutter on the measurement, and about halfway through a third target spawns off one of the two targets.
I made a few changes, either because I couldn"t understand how Vo&Ma did it, or because I wanted to make it closer to my target problem. I extended the measurement vector to include both target position AND velocity (the filter they describe tracks position and velocity but only observes position). Velocity is observed as just being dx/dt, change in position over time, between this new observation and the previous position of this target. Targets are either birthed or spawned depending on which initialisation weight function would give a higher weight; they are given the appropriate initialisation covaria
文 件 列 表
GM_PHD_Filter
CalculateOSPAMetric.m
Test_Jacobian_Calculation.m
error_ellipse.m
GM_PHD_Initialisation.m
GM_EKF_PHD_Simulate_Measurements.m
README.txt
GM_PHD_Simulate_Initialise.m
GM_PHD_Simulate_Measurements.m
Calculate_Jacobian_H.m
ConvertPlusMinusPi.m
GM_PHD_Predict_Birth.m
GM_PHD_Construct_Update_Components.m
GM_EKF_PHD_Update.m
GM_EKF_PHD_Simulate_Plot.m
GM_EKF_PHD_Simulate_Initialise.m
GM_PHD_Filter.m
GM_PHD_Estimate.m
GM_EKF_PHD_Predict_Birth.m
unifpdf_2d.m
GM_PHD_Simulate_Plot.m
GM_PHD_Calculate_Performance_Metric.m
GM_EKF_PHD_Construct_Update_Components.m
GM_EKF_PHD_Initialise_Jacobians.m
GM_EKF_PHD_Predict_Existing.m
ReleaseNotes.txt
GM_PHD_Update.m
GM_PHD_Create_Birth.m
GM_PHD_Prune.m
GM_EKF_PHD_Create_Birth.m
GM_PHD_Predict_Existing.m
Older_Version_GM_PHD_Filter_Without_EKF
GM_PHD_Filter_v105.zip
license.txt
