V.Yu. Kiselev - Post-graduate Student, Saint Petersburg State University of Aerospace Instrumentation E-mail: vukis@bk.ru А.А. Monakov - Dr.Sc. (Eng.), Professor, Saint Petersburg State University of Aerospace Instrumentation E-mail: a_monakov@mail.ru

With the air traffic and surveillance facilities evolvement many theoretical issues of tracking systems synthesis have been covered but not the ones assessment problems. To determine tracking performance it is necessary to have appropriate quality indicators and me-thods for their quantification. The paper is third in a series of publications about quality indicators and methods of their statistical evaluation, which is based on mathematical modeling of algorithms used in different stages of aircraft tracking. The paper covers problems of track filtering and extrapolation algorithms assessment. The proposed quality indicators consists of two sets. The first set includes metrics characterizing the fluctuation component of the track estimation error. The second one includes metrics characterizing the dynamic component of the track estimation error. The technique of the experiment on evaluation of quality indicators is based on aircraft radar tracking modeling in scenarios typical for air traffic control systems. Simulation results are present in the form of dependency graphs on track quality metrics of radar quality metrics. Proposed metrics and techniques of their evaluation were used to obtain the experimental curves characterizing the estimation quality of eight algorithms: Kalman filter, extended Kalman filter, unscented Kalman filter, particle filter, interactive multiple model Kalman filter, interactive multiple model extended Kalman filter , interactive multiple model unscented Kalman filter and interactive multiple model particle filter. We made a comparative analysis of the algorithms in terms of aircraft-s motion parameters estimation accuracy both on flight path without changing mode of movement and in with the change of movement mode. The dependency graphs allow recommending filtering algorithm based on quality requirements for tracking system.

 

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