Multisource information fusion for enhanced simultaneous tracking and recognition

Abstract

A layered sensing approach helps to mitigate sensor, target, and environmental operating conditions affecting target tracking and recognition performance. Radar sensors provide standoff sensing capabilities over a range of weather conditions; however, operating conditions such as obscuration can hinder radar target tracking. By using other sensing modalities such as electro-optical (EO) building cameras or eye witness reports, continuous target tracking and recognition may be achieved when radar data is unavailable. Information fusion is necessary to associate independent multisource data to ensure accurate target track and identification is maintained. Exploiting the unique information obtained from multiple sensor modalities with nonsensor sources will enhance vehicle track and recognition performance and increase confidence in the reported results by providing confirmation of target tracks when multiple sources have overlapping coverage of the vehicle of interest. The author uses a fusion performance model in conjunction with a tracking and recognition performance model to assess which combination of information sources produce the greatest gains for both urban and rural environments for a typical sized ground vehicle.

Application of VNIIRS for target tracking

“…Airborne tracking and classification (target) [37,38,39], track and recognition (type) [40] and simultaneous tracking and identification (allegiance) [41,42] of high value ground targets is a difficult task impacted by operating conditions. Layered sensing, using a combination of standoff and shortrange sensors, maintains target track and classification in cluttered environments such as cities or densely vegetated areas through sensor diversity.…”