A Closed-Form Prediction Update for Extended Target Tracking Using Random Matrices
Nathan Bartlett, Christopher Renton, Adrian Wills
Abstract
This paper proposes a new class of state transition models that afford closed-form predictions for the tracking of extended targets. A key innovation is to employ a non-central inverse Wishart distribution to model the state transition density of the target extent. Importantly, this results in a simplified prediction update that is computationally efficient and improves target tracking performance when compared to state-of-the-art alternatives on standard simulation scenarios.
Topics & Concepts
Wishart distributionTracking (education)Computer scienceKey (lock)State (computer science)Inverse-Wishart distributionInverseAlgorithmArtificial intelligenceMathematicsMachine learningComputer securityPsychologyPedagogyMultivariate statisticsGeometryTarget Tracking and Data Fusion in Sensor NetworksStatistical Mechanics and EntropyDistributed Sensor Networks and Detection Algorithms