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.
Highlights
T HE frequency hopping (FH) communication is highly valued and developing rapidly in military communications in terms of its a series of advantages, e.g., easy implementation of code division multiple access, low probability of interception, high spectrum utilization, and strong anti-fading ability [1]
The blind source separation (BSS) algorithm refers to the separation of signals based on the independence or sparsity of the signal itself, without knowing any prior information. [3] [4] study FH signal network station sorting based on independent component analysis (ICA), but the proposed algorithms can not perform well when SNR is low and require more antenna arrays than the number of signal sources
FH SIGNAL SORTING we analyze the classical clustering algorithm and propose a network station sorting scheme based on neural network to achieve better performance
Summary
T HE frequency hopping (FH) communication is highly valued and developing rapidly in military communications in terms of its a series of advantages, e.g., easy implementation of code division multiple access, low probability of interception, high spectrum utilization, and strong anti-fading ability [1]. [3] [4] study FH signal network station sorting based on independent component analysis (ICA), but the proposed algorithms can not perform well when SNR is low and require more antenna arrays than the number of signal sources. It performs parameter estimation according to the timefrequency spectrum, and utilizes the clustering algorithm to sort the frequency hopping description words (HDW) of signals. Separating signals from multiple FH network from mixed signals is still an indispensable problem in FH communication reconnaissance, and there are few applications in the field of the sorting algorithm based on parameter estimation. In order to meet the accuracy and real-time requirements of frequency hopping signal sorting, we consider a noncooperative FH communication system model as shown, and propose a neural network-based method for hybrid FH signals sorting. Simulation results show that compared with clustering algorithms, it can achieve better sorting accuracy, especially in low signal-to-noise ratio environment
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