Feature Fusion Based on Bayesian Decision Theory for Radar Deception Jamming Recognition

Abstract

As an important part of electronic warfare, radar countermeasure determines the trend of war to a large extent. Modern radar jamming technology, especially deception jamming technology, plays an increasingly important role. Therefore, how to identify radar deception jamming is very necessary. In this paper, a feature fusion algorithm based on Bayesian decision theory is used to recognize radar deception jamming signals. Firstly, the real echo signal, deception jamming signal (contains range gate pull-off jamming, velocity gate pull-off jamming and range-velocity gate pull-off jamming) and noise signal received by radar are acquired as signal sources. Then bispectrum transformation is used to extract features in several aspects. Finally, kernel density estimation is used to improve the fusion algorithm, and the feature fusion algorithm based on Bayesian decision theory is used to recognize the received signals from radar. Results of the experiment indicate that the algorithm not only can recognize the radar deception jamming, but also has high accuracy.

Keywords

• Bayesian decision theory
• bispectrum transformation
• deception jamming
• feature fusion
• kernel density estimation