PHD Multi-Target Tracking Simulation

This article presents a PHD algorithm-based multi-target tracking simulation method that has proven effective and can be readily executed. PHD filtering is a probability hypothesis density approach for multi-target tracking that utilizes random sets to represent multiple target states and observations, integrating them into iterative Bayesian filtering. The implementation typically involves propagating the intensity function through prediction and update steps, where Gaussian mixture models or particle filters are commonly used for practical computation. Compared to traditional methods, PHD filtering avoids explicit state-observation associations, resulting in superior tracking performance. Furthermore, PHD filtering has brought significant transformations to multi-target tracking technology, expanding its application scope and improving operational efficiency through scalable computational frameworks that handle varying numbers of targets without requiring complex matching logic.