Multiple Kernel Independent Component Analysis for Anti-jamming of Communication Radio

Abstract

The anti-jamming of communication radio has attracted intensive attentions from communication community. The blind source separation (BSS) is much effective when the jamming has entered. The multiple kernel independent component analysis (MKICA) is proposed instead of a single one, which can not only combine multiple kernels corresponding to different notions of similarity or information from multiple feature subsets, but also fuse distinctions of multiple kernels. The core of algorithm is the kernel canonical correlation analysis (KCCA), where the efficient use of multiple kernel trick makes it more suitable for different sources and more robust for different distributions. Hence, there will be a remarkable improvement of separation accuracy. Numerical experiments based on the artificially synthesized data and intermediate frequency (IF) signal containing clear voice demonstrate state-of-the-art performances of proposed algorithm. It is ideal for separation of useful communication signals when confronting with various jamming in the anti-jamming field.