Signal Sorting Algorithm of Hybrid Frequency Hopping Network Station Based on Neural Network

Abstract

In non-cooperative frequency hopping communication system, the frequency hopping network station sorting of the received hybrid signals plays an important role and becomes an active research area in recent years. In order to solve the problem that the currently widely used clustering algorithm cannot achieve satisfactory accuracy. In this paper, we propose a signal sorting method for hybrid frequency hopping network stations by applying the neural network to classify the frequency hopping description words of signals. Additionally, the conjugate gradient algorithm is utilized in the neural network training process to improve the convergence speed. Once the neural network training is finished, only one frequency hopping description word of the input signal is required to obtain its own network station label in real time. Simulation results demonstrate that when compared with the clustering algorithm, the proposed algorithm converges with less iterations and delivers better sorting accuracy, especially in a low signal to noise ratio environment.

Keywords

• Frequency hopping communication
• signal sorting
• neural network
• Kmeans clustering
• conjugate gradient algorithm