Estimating the performance of a multiradar tracker using machine learning

Abstract

Multiradar tracking of aircraft is a sensor fusion problem including complicated measurement and object motion models. The accuracy of radar measurements differs significantly between bearing and range. The transformation of the measurements produced by individual radars between the local and a common reference frame include multiple calibration and bias compensation steps that introduce systematic errors to the measurements. Most of the common tracking algorithms require linearization of the possibly nonlinear measurement and motion models, introducing an additional error source. Understanding the quality of information provided by a system is essential for situational awareness and decision making. In this thesis, the altitude component of the location is approached with data-driven machine learning methods. Deep learning and Support Vector Machine (SVM) methods are proposed for providing a temporal quality estimate of the tracker altitude output. Furthermore, the deep learning method is refined to predict correction terms for the tracker altitude output, essentially improving the tracking accuracy. The developed methods were trained and tested with data from an experimental multiradar tracking system. Although the performance of the methods is most likely highly specific to the system and parametrization, the results show that especially the deep learning method provided a fairly accurate error estimate and that the correction terms increased the altitude accuracy significantly. Obtained results show that machine learning methods can be useful in radar tracking even with a relatively small amount of training data.