Aggregation of Composition States for Markov Estimation In Level 2 Fusion

摘要

In sensor fusion, the use of composition information can help define and understand relationships between targets. This process, part of the Situational Assessment problem, , also referred to as Level 2 fusion, can be quite complex as a result of standard classification approaches such as the Bayesian taxonomy. Determination of the number and type of elements that comprise a group can vary from report to report based on the type of sensors, the environment, and the group's behavior. Estimation of group composition that can take these factors into account has been developed using a Markov chain approach. If the number of potenital target classes is significant and the various standard group compositions are numerous, the computation complexity becomes unmanageable. In this effort, the use of state aggregation is investigated to provide a useful and a computationally attainable Level 2 composition state estimate.