Classification Of Radar Platform Motion Types

The detection of an emitting radar and accurate tracking of the associated platform direction are the most important goals of Electronic warfare (EW) systems. EW systems use Kalman filter in order to compansate the measurement errors that occur during instantaneous direction measurements of detected radars. The compatability of the Kalman filter prediction model and parameters with the platform motion type improves the quality of the values shown on the screen. In this paper, it is aimed to determine the platform types associated with the radars using radar parameters that are measured by EW systems. First, an efficient pre-processing method including quantization for interval values and grouping of class values is applied to improve classification performance. Then, Multi-task Learning (MTL) neural network and Support Vector Machine (SVM) techniques are applied to the problem. It is observed that SVM technique outperforms MTL technique. It is evaluated that the result of the classification process can be utilized for selecting the type and the parameters of the filter used for tracking.