The Application of Performance Metrics to Staring radar for Drone Surveillance
Mohammed Jahangir, Bashar I. Ahmad, Chris J. Baker
Abstract
In this paper, several performance metrics are proposed for staring radar to provide figures of merit that effectively capture the overall capability of a non-cooperative drone surveillance system. Such figures of merit can offer more meaningful system performance measures to the end user by combining aspects such as track quality combined with target classification. This is contrary to relying only on standard classifier performance metrics such as a confusion matrix. Example results are presented here using real radar data.
Topics & Concepts
StaringComputer scienceDroneRadarConfusionClassifier (UML)Radar trackerArtificial intelligenceRadar systemsConfusion matrixReal-time computingTelecommunicationsBiologyGeneticsSociologyPsychoanalysisCommunicationPsychologyRadar Systems and Signal ProcessingAdvanced SAR Imaging TechniquesTarget Tracking and Data Fusion in Sensor Networks