With the widespread application of global navigation satellite system(GNSS)in various fields

the threat posed by spoofing interference is becoming increasingly severe.Traditional spoofing interference detection methods suffer from low detection accuracy and high false alarm rates in complex electromagnetic environments.In this paper

a spoofing interference detection and identification algorithm based on the fusion of GNSS satellite signal-to-noise ratio(SNR)and principal-features independent component analysis(ICA)is proposed.The algorithm first employs SNR-based pre-screening to narrow the signal processing scope

then utilizes principal-feature ICA to optimize signal separation

and finally identifies interference types by exploiting SNR differences.The study demonstrates that the proposed fusion algorithm achieves a detection and identification rate of over 90% in medium to high SNR environments

which is significantly higher than the 85% of the traditional principal-feature ICA algorithm.The proposed algorithm has the advantages of low computational complexity and good real-time performance

providing effective technical support for the security protection of GNSS.