The invention relates to the technical field of
signal analysis, in particular to a GNSS (Global Navigation
Satellite System) detection method and
system based on
machine learning, and the method comprises the steps: receiving a navigation radio-frequency
signal containing reality and deception, and carrying out the down-conversion of an RF (
Radio Frequency) front end to obtain intermediate-
frequency data; gNSS
signal capture is completed by searching correlation peaks in frequency and code phases based on
intermediate frequency data; in the capturing stage, a motion state, a correlation peak number, a
noise value, capturing parameters and peak + / -1
chip sampling features are extracted and input into a
machine learning classification model to judge whether
cheating exists or not; if so, separating the deception signal from the
real signal, and respectively sending the deception signal and the
real signal into independent tracking channels; measuring an
array element carrier
relative phase by an array antenna, establishing a
phase center model, and correcting to obtain an effective
phase center and a differential baseline; according to the method, a deception
azimuth angle and a
pitch angle are obtained, a beam weight is calculated, a deception direction is subjected to null setting, a real direction is subjected to
main lobe protection, and a
weight value is fed back in real time to improve a signal carrier-to-
noise ratio and locking stability. According to the invention, the problem that the existing commercial GNSS signal is low in power and easy to be interfered by deception is solved.