Operational tracking issues in the presence of Closely Spaced Objects (CSOs)

摘要

The real world presents a much messier tracking environment than the usual pristine world of tracking simulations. In particular, simulations often do not properly account for the presence of CSOs in the vicinity of the objects being tracked, thereby producing potentially misleading results. CSOs have several nasty effects on trackers, which, if not mitigated, may result in show-stopping surprises when attempting to use a tracker operationally. This paper will describe and quantify some of the limitations that CSOs impose on single sensor and/or multisensor trackers. These limitations include delaying the expected time that a given object can be confidently resolved, interfering with the ability to associate objects between sensors properly, contaminating track files with spurious signature information, and forcing some form of cluster tracking to be employed. Analytic predictions of these limitations, based on local object density over time, will be presented, backed up by various Monte Carlo simulations. A more robust metric is proposed to allow the prediction of a more operationally meaningful probability of correct target association in a multisensor environment.