A hybrid data association model for efficient multi-target maximum likelihood estimation

A key challenge in multi-target tracking is that the number of possible measurement-to-target associations grows exponentially with the number of targets. The popular PMHT approach bypasses this problem by using an arguably wrong assignment model that, however, allows evaluating the likelihood function with complexity linear both in numbers of targets and of measurements. Unfortunately, the resulting tracking quality may suffer due the wrong assignment model. In this paper, we propose a hybrid data association model that combines both the PMHT and original models. In this vein, the likelihood function can be evaluated efficiently in polynomial time while still providing tracking results close to the exact (but, in large scale cases, intractable) solution resulting from the original “correct” model. The feasibility of the new hybrid assignment model is demonstrated by means of maximum likelihood estimation of closely-spaced targets. Extension to marginalized probability calculation - that is, the joint probabilistic data association filter (JPDAF) [1] is in [2].