Multiple Target Tracking With Unresolved Measurements
Robert Blair Angle, Roy L. Streit, Murat Efe
Abstract
A multiple target tracking filter is developed for merged measurement problems that arise with finite resolution sensors. The resulting combinatorial problem is incorporated directly in the joint likelihood function using analytic combinatorics techniques. The Bayesian filter is a sum of several terms that correspond one-to-one to the set of all feasible hypotheses about measurements and targets, i.e., resolved/unresolved, detected/undetected. Performance is demonstrated for two targets in both crossing and parallel target motion scenarios.
Topics & Concepts
Tracking (education)Filter (signal processing)Computer scienceSet (abstract data type)Bayesian probabilityAlgorithmLikelihood functionRadar trackerResolution (logic)Artificial intelligenceFinite setPattern recognition (psychology)MathematicsComputer visionEstimation theoryPsychologyMathematical analysisProgramming languagePedagogyRadarTelecommunicationsTarget Tracking and Data Fusion in Sensor NetworksInfrared Target Detection MethodologiesAdvanced Optical Sensing Technologies