Multiple-Target Tracking and Identity Management with Application to Aircraft Tracking

The problem of tracking and managing the identities of multiple targets is discussed and applied to the passive radar tracking of aircraft. The targets are assumed to be commercial aircraft switching modes during flight, and are thus well modeled by hybrid systems. We propose a computationally efficient algorithm based on joint probabilistic data association for target-measurement correlation. We use the results of this algorithm to simultaneously implement an identity management algorithm based on identity-mass flow, and a multiple-target tracking algorithm based on the residual-mean interacting multiple model algorithm. Together, they constitute the multiple-target tracking and identity management algorithm. The multiple-target tracking and identity management algorithm incorporates suitable local information about target identity, when available, in a manner that decreases the uncertainty in the system as measured by its statistical entropy. For situations in which local information is not explicitly available, a technique based on multiple hypothesis testing is proposed to infer such information. This algorithm allows us to track multiple targets, each capable of multiple modes of operation, in the presence of continuous process noise and of spurious measurements. The multiple-target tracking and identity management algorithm is demonstrated through various scenarios that are motivated by air traffic surveillance applications.

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