Study On The Blind Equalization Method Of Shortwave Burst Signals Based On Relevance Vector Machine

In the shortwave communication system, the Inter-Symbol Interference(ISI) is often encountered so that sending signals cannot be recognized correctly by receiving terminal, which because sending signals are interfered by time delay, multipath fading and noise, etc.. Channel equalization is the main technology to eliminate or reduce ISI. Blind equalization is a signal processing technique without resorting to the training sequence, which could suppress ISI and recover the input signals. With better security, anti-jamming and anti-interception capability, shortwave burst communication is widely used in the military communicate field. Comparing with the traditional shortwave signal, shortwave burst signal has less symbolic number, usually a few hundred, sometimes only a few decades. However, less data symbols put forward higher requirement for the blind equalization.Higher-order statistics(HOS) is the most classical method in the blind equalization, but it has a a large amount of calculation by using the characteristics of HOS and needs plenty of data to be convergent in slow speed, which restrains blind equalization to be used in small data signal processing field. In recent years, machine learning technologies are widely applied in the blind equalization, such as Support Vector Machine(SVM), Relevance Vector Machine(RVM), etc.. The blind equalization based on SVM and RVM can meet the required equilibrium levels with less sample data than that based on HOS. Basing on the Bayesian framework, RVM equalizer has better convergence and sparser detection model compared with SVM.Usaually, SVM or RVM uses single kernel function, and can’t get interpolation and extrapolation ability simultaneously, moreover, the choice of kernel function influences the performance of equalizer greatly. Therefore, a Hybrid-kernel Relevance Vector Machine(Hk-RVM) which uses Hybrid-kernel to generate design matrix is proposed in this paper in order to make the Hk-RVM get superior interpolation and extrapolation ability than traditional RVM.We study the basic principle of blind equalization, analyse traditional method of blind equalization and it’s advantages and disadvantages. Detailing RVM’s principle and the blind equalization method based on RVM, then make plenty of simulation experiments. Proposing a blind equalization theory and algorithm based on Hk-RVM, compare performances of the blind equalization algorithm’s basde on Hk-RVM with RVM and SVM, respectively. The experiment results show that Hk-RVM equalizer is sparser and has lower bit-error rate than RVM and SVM equalizer. Furthermore, because of the Bayesian framework, the blind equalization based on Hk-RVM and RVM have higher evolutionary stability than that on SVM.