On Adaptive Probability Hypothesis Density Filter for Multi-target Tracking

摘要

In order to improve tracking performance of the existing probability hypothesis density (PHD) filters, we present an adaptive filter for multi-target tracking in this paper. At first, both the completed clutter process and the single-target measurement likelihood are improved based on the Bayesian theory. Then, the target cardinality is corrected using the adaptive detection gate. What's more, a novel particle implementation is explored step by step. Numerical study results have been carried out to confirm the promising tracking performance of the proposed PHD filter.