Gorishna I. The training providing of modeling of the adaptive radar sensing in the conditions of hindrances

The dissertation is devoted to improving the performance of the operator of the training complex when determining targets on the underlying surface of the on-board radar sensing zone. Object modeling was proposed in order to operate under dynamic conditions similar to full-scale ones using the self-organizing process of maximizing the signal-to-noise ratio to highlight useful signals in order to visualize objects about the environment during adaptive radar sensing under noisy conditions. An ergatic method of self-learning in relation to a signal / filter pair is proposed for suppressing unwanted side lobes with the aim of improving the processes of detection and identification of targets by the operator of the training complex. The qualitative characteristics and stages of the space-time signal processing in the radar are justified under the conditions of a consistent cross-correlation function between the received and expected signals.