Design and Implementation of Blind Source Separation Based on BP Neural Network in Space-Based AIS

Chengjie Li,Zhongqiang Luo,Yilun Liu,Ying Yang,Zhen Zhang,Lidong Zhu

doi:10.3389/frspt.2021.756478

Abstract

In space-based AIS (Automatic Identification System), due to the high orbit and wide coverage of the satellite, there are many self-organizing communities within the observation range of the satellite, and the signals will inevitably conflict, which reduces the probability of ship detection. In this paper, to improve system processing power and security, according to the characteristics of neural network that can efficiently find the optimal solution of a problem, proposes a method that combines the problem of blind source separation with BP neural network, using the generated suitable data set to train the neural network, thereby automatically generating a traditional blind signal separation algorithm with a more stable separation effect. At last, through the simulation results of combining the blind source separation problem with BP neural network, the performance and stability of the space-based AIS can be effectively improved.

Highlights

In space-based AIS (Automatic Identification System), satellite communications are one of important parts, due to the satellite orbit altitude is relatively high and the number of satellites increasing, the openness of satellite orbits makes satellite communications vulnerable to be jammed from external signals
In the data training process, 70% of the data set is used as the training set, 15% of the data set as the verification set and the remaining 15% of the data set as the test set
The results show that: 1) The invulnerability performance of space-based AIS network under random attack has significant advantages than the DE (Differential Evolution algorithm) network optimization and PSO (Particle Swarm Optimization) network optimization; 2) As the number of nodes increases, the invulnerability of the system decreases; 3) The invulnerability performance of space-based AIS network with blind source separation was significantly better than that without blind source separation (Yang et al, 2019) (Tai et al, 2017)

Summary

Introduction

In space-based AIS (Automatic Identification System), satellite communications are one of important parts, due to the satellite orbit altitude is relatively high and the number of satellites increasing, the openness of satellite orbits makes satellite communications vulnerable to be jammed from external signals. To solve that problem, improving the efficiency of information processing, new technologies must be developed. Blind separation technology can improve the efficiency of signal processing without occupying excess bandwidth. With the increasing processing power of computers, neural network technology has been introduced into blind separation technology (Lee et al, 2008; Lahat and Christian, 2016; Fu et al, 2014). In (Li et al, 2020), separate the mixed source signals by Particle swarm optimization

Objectives

Results

Conclusion