Research And Optimization Of Parameter Estimation Algorithm For GNSS Signal Under Spoofing Attack

The spoofing interference is one of the great threats of satellite navigation systems,which affects the safe driving of aircraft and the positioning services of communication equipment.Therefore,in the face of the application requirements of ensuring the availability and reliability of receivers,it is of great engineering value to study the signal parameter estimation algorithm under spoofing attacks.Firstly,the paper starts from the model research of the spoofing signal,and studies the three classical signal parameter estimation algorithms for the use of the traditional code tracking loop for the error of the code phase estimation of the repeater spoofing,and the ability to continuously track the authentic signal,TK(Teager-Kaiser)operator,Coupled Amplitude and Delay Lock(CADLL)algorithm and Multipath Estimating Delay Lock Loop(MEDLL)algorithm,and through three estimations The simulation implementation of the algorithm and the analysis of the performance and disadvantages of the algorithm,choose the MEDLL algorithm with better estimation effect to carry out the parameter estimation algorithm of the main body.At the same time,aiming at the problem that the classic MEDLL has a large error in the signal with low carrier-to-noise ratio,the estimation of the number of spoofing signals and the high complexity of the algorithm,the MEDLL algorithm based on coherent integral optimization and multi-channel spoofing are studied respectively.The signal adaptive path number identification algorithm and the unequal interval sampling MEDLL based on predictive feedback improve the estimation effect of MEDLL algorithm under low carrier-to-noise ratio,reduce the time complexity of MEDLL algorithm,and improve the fastness of the algorithm.The number of deception signals is adaptively estimated and identified.Then,aiming at the problem that the classical MEDLL algorithm does not evaluate the signal parameters under the short delay spoofing jamming,the neural network optimization based short delay spoofing signal parameter estimation algorithm is proposed.The algorithm is based on the neural network regression prediction theory.The estimation method based on delay estimation algorithm dynamically switches the MEDLL algorithm and the neural network optimization based estimation algorithm,and uses BP neural network and RBF neural network to estimate the parameters of short delay and dynamic spoofing interference respectively.The advantages and disadvantages of BP neural network and RBF neural network algorithm are summarized and analyzed.Finally,aiming at the problem of setting different parameters of the spoofing signal in complex situations,a comprehensive estimation platform for spoofing signal parameters is constructed in a targeted manner.The spoofing interference including the multi-channel spoofing signal and the traction spoofing are analyzed by the signal parameter estimation part.It verifies the estimation accuracy,applicability and reliability of the improved optimized MEDLL algorithm.