Real-Time Tracking Of Hundreds Of Targets With Efficient Exact JPDAF Implementation

Abstract

An assignment problem is considered with the constraint that the same hypothesis cannot be applied to more than one object. We desire efficiency without approximation. Multiple target tracking methods such as the joint probabilistic association filter (JPDAF) motivate us. Methods of solving this assignment problem involving enumerating all possible joint assignments is infeasible except for small problems. A recent approach circumvents this combinatorial explosion by representing the structure of the target hypotheses in a `net' which exploits redundancy in an ordered list of objects us to describe the problem. Here, we generalize this approach to process the objects in a tree structure this exploits conditional independence between subsets of the objects. This gives a substantial computational saving and allows us to consider scenarios which were previously impractical. In particular, we show the feasibility of using an exact JPDAF implementation to track 400 targets