Deep Reinforcement Learning Based Decision Making for Complex Jamming Waveforms

Wideband Waveform Design for Distributed Precision Jamming

Precision electronic warfare is a hot direction for future jamming technology development, and distributed precision jamming (DIPJ) is one of its typical application scenarios. The task objective of DIPJ is to design jamming waveforms so that the jamming energy generated by a set of ultra-sparse array transmitters can be focused in the jamming region of interest while being suppressed in other specific protected regions, which can be viewed as a distributed multiple-input and multiple-output system waveform design problem under a three-dimensional scenario. This paper extends the jamming signal model in DIPJ from narrowband to wideband based on previous work to address a broader range of jamming tasks. After extending the model to wideband signals, a method based on the traditional maximum total energy difference optimization objective is first given for comparison. A wideband jamming waveform design method based on the majorization minimization algorithm with the desired power spectrum matching as the optimization target is designed for the problem that the maximum energy difference method cannot focus energy well in the jamming region. The simulation results show that the presented method can make the jamming energy well concentrated in the target region and evenly distributed over the whole bandwidth, while the energy in the whole bandwidth is suppressed in the protected region.