GPS Anti-spoofing Method Based On Adaptive Kalman Filter

In recent years,global positioning system(GPS)has rapidly developed and applied in various fields,not only in the military field,but also in the fields of economic construction and scientific experiment.Thus,the research about GPS system plays an increasingly important role in our daily life.However,the GPS navigation system faces a complex and poor channel environment.As GPS signal is vulnerable to various forms of intentional or unintentional interference,among them spoofing jamming is similar to GPS signal and the interference power is small,providing good concealment.Therefore,how to realize an effective and accurate GPS anti-jamming method has attracted more and more attention.This paper focuses on GPS spoofing interference,and deeply studies the GPS anti-spoofing scheme based on improved extended Kalman filter.First of all,the signal received by the receiver is affected by spoofing jamming,which leads to some error in the measurement of pseudo range and is bad for navigation positioning.This paper proposes an improved algorithm based on Sage-Husa adaptive extended Kalman filter,this method firstly established the system model,and then construct the observation vector,after that,detected whether there's spoofing jamming in the process of positioning solution of GPS according to the nature of innovation orthogonality of Kalman filter.If there exists spoofing jamming,by the introduction of Sage-Husa adaptive extended Kalman filter algorithm and using the activation function of BP neural network,correct the update process,so as to restrain the influence of spoofing jamming and achieve the purpose of anti-spoofing.The simulation results show that the algorithm has better anti-spoofing ability and can effectively improve the positioning accuracy.Secondly,according to the interference intensity of different Kalman filter in GPS anti-spoofing system is different or a single Kalman filter may be faulty,leading to the entire system can not work properly,this paper presents an improved algorithm for multi-Kalman data fusion based on evidence theory(Dempste Shafer,DS).Using evidence theory to deal with conflict evidence,quantitative conflict,assign weights to each Kalman filter,and then get the final filtering result through data fusion.Simulation results and analyses demonstrate that the proposed algorithm effectively improves the anti-spoofing ability of the GPS system,raises the tracking accuracy,and further improves the integrality of the GPS anti-spoofing system.The above two aspects of the study are all in a low dynamic environment,this paper finally for high dynamic,GPS receiver is affected by spoofing interference causing the filtering results seriously offset or even divergent situation.This paper proposes an anti-spoofing algorithm based on adaptive Kalman filter for high dynamic positioning.The method starts from following two perspectives,firstly if there exists spoofing jamming,use the M-estimation in statistics,and the error variance will be reduced to ensure that the filter estimate is as close as possible to the true state of the system,meanwhile guaranteeing the filtering accuracy of the results.At the same time,the introduction of the fading factor reduces the proportion of the previous state information and reuses current measurement information,which ensures liable convergence filter and improves stability and further achieves the target of anti-spoofing in the process of high dynamic positioning.The simulation results show that in high dynamic environment,if spoofing jamming exists,the proposed method can guarantee the accuracy and stability of the positioning results at the same time.