Multiple hypothesis tracking for multiple target tracking

TLDR

Multiple hypothesis tracking (MHT) is the preferred method for solving the data‑association problem in modern multiple target tracking systems. The paper reviews MHT motivations, core principles, common implementations, and discusses current applications and future research directions. MHT combines multiple data‑association hypotheses with filter models, including the interacting multiple model (IMM), and offers alternative implementations. Studies demonstrate that MHT outperforms single‑hypothesis approaches in multiple target tracking.

Abstract

Multiple hypothesis tracking (MHT) is generally accepted as the preferred method for solving the data association problem in modern multiple target tracking (MTT) systems. This paper summarizes the motivations for MHT, the basic principles behind MHT and the alternative implementations in common use. It discusses the manner in which the multiple data association hypotheses formed by MHT can be combined with multiple filter models, such as used by the interacting multiple model (IMM) method. An overview of the studies that show the advantages of MHT over the conventional single hypothesis approach is given. Important current applications and areas of future research and development for MHT are discussed.