Performance Evaluation of Deception Against Synthetic Aperture Radar Based on Multifeature Fusion

Abstract

Deception is an effective means of jamming against synthetic aperture radar (SAR). The performance of deceptive jamming is affected by the accuracy of parameter measurement and the applied antijamming methods of SAR. In this article, we analyze the accuracy of current deceptive jamming evaluation indicators, propose a new method for evaluating the performance of deceptive jamming based on the combination of typical dominant deceptive jamming evaluation indicators, and recessive indicators extracted by convolutional neural networks, and obtain a level of deceptive jamming using a softmax activation function in a fully connected network. Deceptive jamming images affected by the SAR motion parameter measurement error are taken as training and test sets. Finally, the moving and stationary target acquisition and recognition database is used as a deceptive jamming template in order to verify the effectiveness of the proposed method.