PHD Multi-Target Tracking Simulation

An executable multi-target tracking simulation implemented using the Probability Hypothesis Density (PHD) algorithm. The PHD filter represents multi-target states and observations as random sets, which are then incorporated into Bayesian filtering iterations, eliminating the need for explicit data association and revolutionizing multi-target tracking technology.

Comparison of Particle Filter and PHD Multi-Target Tracking

Comparative analysis of particle filter and marginalPHD filter for multi-target tracking with three targets, including tracking performance evaluation and implementation considerations.