GNSS deception jamming detection method and system in signal acquisition phase

Abstract

The invention discloses a GNSS deception jamming detection method and system in a signal acquisition phase. The method comprises the following steps: 1, in the signal acquisition phase, the characteristic parameters of a preset number of two-dimensional search arrays with Doppler frequency shift and code phase as axes generated by a receiver are extracted, and sample data are acquired; 2, throughthe training data in the sample data acquired in the first step, a pre-built classification neural network model is trained, and the well-trained classification neural network model is acquired; and 3, a to-be-detected GNSS signal is inputted to the well-trained classification neural network model acquired in the second step, and a detection result is outputted through the classification neural network model. The detection method of the invention integrates signal power, the number of correlation peaks and related peak deformity parameters for unified detection, and has the advantages of gooddetection performance, strong applicability, high timing and low complexity.

Description

technical field [0001] The invention belongs to the technical field of interference detection in satellite navigation systems, and in particular relates to a GNSS deception interference detection method and system in a signal acquisition stage. Background technique [0002] Global Navigation Satellite System (GNSS) is a wide-coverage, all-weather, real-time, high-precision navigation system. With the continuous development of satellite navigation technology, GNSS is widely used in various military and civilian facilities. The number of users and application scenarios continue to increase, and people pay more and more attention to safety and reliability. The security threats faced by the current satellite navigation system can be mainly divided into unintentional interference and intentional interference. Intentional interference mainly refers to artificial malicious interference, and can be divided into suppressive interference, deceptive interference and combined interfere...

AI Technical Summary

Problems solved by technology

The accuracy of signal absolute power detection and carrier-to-noise ratio detection is poor, and the method of detecting the number of correlation peaks during signal capture is difficult to distinguish when the phase difference between the spoofed signal and the real signal is less than 2 chips

The method of analyzing the output of the correlator during signal tracking cannot make full use of the signal information, and the detection effect is related to the selected parameters. The detection accuracy is low when the signal receiving power is unstable and the signal-to-noise ratio is small.

Images