A simulation system for feature-aided tracking research

摘要

Many years of tracking research have shown that the greatest obstacle to effective track estimation is accurately associating sensor kinematic reports to known tracks, new tracks, or clutter. Errors in report association occur more frequently under increasingly stressful conditions, like closely-spaced targets and low measurement rates, which can lead to unstable and even divergent tracking performance. It is widely expected that adding target features will aid report association and result in enhanced track accuracy and lengthened track life. Although sensors can provide features to enhance association, progress in implementing feature aiding has been slowed by the lack of data and tools that could assist exploration and algorithm development. To encourage research in this important discipline, the Sensors Directorate of the Air Force Research Laboratory (AFRL/SN) is sponsoring a challenge problem called Feature-Aided Tracking of Stop-move Objects (FATSO). FATSO's long-range goal is to provide a full suite of public data and software to promote explorations into viable methods of feature aiding. This paper introduces the FATSO project, focusing on an upcoming release that will contain data from a diverse target set and predictor software for generating radar signatures.