Research On Spoofing Interference Detection Techniques Of Global Navigation Satellite Systems

Global Navigation Satellite System(GNSS)is popular in navigation and timing applications because of its accuracy,low cost and global nature.GNSS signals in the civil band are susceptible to all types of intentional and unintentional interference due to their open signal structure and extremely low signal power.Among them,the spoofing interference can manipulate the user’s navigation information without the user’s receiving equipment noticing,which has higher concealment and harm compared with other interference.Therefore,it becomes critical to study GNSS spoofing interference detection techniques to equip GNSS user devices with spoofing defense capabilities.Most spoofing interference detection methods at this stage use multiple antenna techniques and combined navigation techniques,which require additional hardware resources and higher costs and are not suitable for promotion among civilian receivers.Without changing the hardware structure of the receiver,this thesis focuses on the detection of the presence of spoofing signals by receiver signal processing techniques.The main work of the thesis is as follows:(1)This thesis investigates the GNSS signal structure and analyses the vulnerability of GNSS signals on this basis.It also elaborates on the system and interference principles of forward spoofing interference,generative spoofing interference and tractor spoofing interference,and designs a spoofing interference detection scheme based on receiver baseband signal processing for the characteristics of small delayed forward spoofing interference and tractor spoofing interference that are the focus of this thesis.(2)To address the problem that the detection method based on the shape of capture correlation peaks cannot distinguish between small delayed spoofing signals and multipath signals,the effects of forwarding spoofing interference signals and multipath signals on the receiver capture results are analyzed,and a small delayed forwarding spoofing interference detection method based on deep learning is proposed on this basis.The thesis uses computer simulation to generate data samples containing authentic signals,spoofing signals,and multipath signals,which are captured by a software receiver,and the two-dimensional capture results are used as the base samples.Set the detection area,intercept the detection matrix in the capture result,transform it into grayscale image by data pre-processing,build the improved LeNet network model,and use grayscale map to train and test the model.Setting different receiver parameters,the detection accuracy reaches up to 98.83%.Experimental results show that the proposed algorithm still has good detection effect on small delayed forwarding spoofing interference in multipath environment.(3)To address the problem that the Signal Quality Monitoring(SQM)indicator deteriorates the detection performance of many specific combinations of relative code phases and carrier phases of the spoofing signal relative to the authentic signal,the SQM indicators with complementary characteristics in the case of relative code phases and relative carrier phases change are jointly detected,and a tractive spoofing interference detection method based on the composite SQM square is proposed.Set the detection window,calculate the composite SQM squared sequence in the window,set the false alarm probability,determine the detection threshold,and estimate whether the receiver is subject to spoofing interference by threshold comparison.The proposed method is compared with the single SQM algorithm and the composite SQM algorithm using TEXBAT(Texas Spoofing Test Battery)spoofing interference data.The experimental results show that the composite SQM square algorithm has better detection performance.