A Comprehensive Survey on GNSS Interferences and the Application of Neural Networks for Anti-jamming

Abstract

In recent years, modern systems are highly reliant on Global Navigation Satellite Systems (GNSS) as reliable positioning, navigation, and timing services have become crucial in many safety-critical, security, and emergency applications. Although GNSS technology offers precise and global positioning and navigation, the GNSS signals are susceptible to intentional and unintentional Radio Frequency Interferences (RFI) as the signal strength is weak when they reach the receiver. Hence, the receiver's performance gets degraded as the interference signal may cause navigation error or saturate the receiver's operation. Therefore, the research on interference mitigation is of high interest to the GNSS community and is emerging rapidly. In order to have a beneficial and extensive outlook, an in-depth survey on GNSS interferences and the application of metaheuristic based neural networks for interference cancellation has been presented in this paper; with an emphasis on one of the major GNSS threats i.e. jamming. Various solutions adopted by the researchers to cope up with the interference have been surveyed and presented. Also, to have a more intuitive insight, a comparative analysis of the existing mitigating techniques has been summarized. In addition, the review focuses on the neural network approach for anti-jamming and also discusses the implementation strategy of particle swarm optimization based back propagation neural network (PSO-BPNN) to address the shortcomings of traditional training algorithms. Finally, the future aspects to further enhance the neural network algorithms for anti-jamming have also been provided for the benefit of researchers.