Research on Multi-Peak Detection of Small Delay Spoofing Signal

Abstract

With the widespread application of GNSS systems in various fields, the problem of spoofing detection has drawn much attention from the satellite navigation community. The GNSS spoofing interference generally uses fake or replayed satellite signals to make the targeted receivers receive false GNSS signals and reduce the accuracy of calculated position and time information. In order to ensure and improve the security of GNSS services, in recent years, academia and industry have studied the spoofing detection technology from multiple aspects, and many theoretical results have been obtained. This paper starts the analysis from the acquisition phase of a receiver and analyzes the characteristics of the smalldelay spoofing signal. Aiming at solving the problem that it is difficult to detect small-delay (0-2 chips) spoofed signals during the acquisition phase, the CNN (Convolutional Neural Network) based method is used to detect the small-delay spoofed signals effectively. According to the experimental simulation results, when the code phase difference between the spoofing signal and the authentic satellite signal is larger than 0.5 code chip, the CNN-based method achieves high detection accuracy. In addition, the algorithm can quickly detect the data without using any additional equipment. Therefore, low complexity is achieved. This makes the algorithm has a good engineering application prospect.INDEX TERMS Acquisition phase, convolutional neural network (CNN), GNSS spoofing detection, small delay.

An Open GNSS Spoofing Data Repository: Characterization and Impact Analysis with FGI-GSRx Open-Source Software-Defined Receiver

“…As both authentic and spoofing signals coexist during the acquisition, the receiver picks up the spoofing signal with the highest peak. This susceptibility is particularly significant in untargeted and meaconing attacks given their untargeted nature, assuming a substantial offset in the code phase between the spoofing and the authentic signals (Li et al, 2020). Although, as illustrated in Figures 20a -20d, the receiver is not spoofed during the tracking stage, acquisition or re-acquisition during ongoing spoofing events may expose the GNSS receiver to potential threats from meaconing and untargeted attacks.…”

Satellite Navigation Spoofing Interference Detection and Direction Finding Based on Array Antenna

“…In 2019, Zhang et al exploited the fact that transposed spoofing interference is a delay to the real satellite signal by combining the observation of the number of correlation peaks with measuring the half-height width size to achieve the detection of spoofing interference with large and small time delays [ 13 ]. In 2020, Li et al proposed a convolutional neural network-based method to effectively detect the presence of small-delay spoofing interference signals, with the method achieving high detection accuracy for small-delay spoofing interference signals [ 14 ]. In 2022, Wang et al used the power combined with the SQM (PCS) method to detect spoofing interference signals and improve detection timeliness [ 15 ].…”

Detection and Orientation of GNSS Spoofing Based on Positioning Solutions of Three Receivers

“…Method ( 5) is a technology to analyze navigation data through machine learning and identify deceptive interference. At present, common detection models include Convolutional Neural Network [21], K-nearest neighbor [22], Support Vector Machine [23], Multi-layer Perceptron Neural Network [24], Probabilistic Neural Network [25], etc. This method has a higher detection rate than traditional spoofing detection technology based on signal characteristics.…”