Regression forest for interference assessment in real ultra short-wave communication jamming system

Interference assessment is a fundamental problem in communication jamming systems, while existing approaches are still far away from being practically used in real applications. With the increasing intensity of jamming signals, the original audio signal is largely covered up and SNR is very low. As a results, traditional assessments are not able to achieve reliable performance. In this paper, we focus on solving the problems from real communication jamming system in practice and for the first time adopt random forests to fuse different objective assessments into the interference assessment system. We propose a new method to solve the synchronous problem, achieve robust objective assessment and disagreements among different objective assessments in a whole framework. We adopt subjective recognition rate to handle the signal damage at the receiver for subjective assessments. We have conducted experimental evaluation on the realistic dataset of communication jamming systems, which demonstrates the effectiveness of the proposed framework. The results have shown that random forests have a generalization ability of fusion of objective measurements.