Research On Gnss Interference Source Tracking Algorithm Based On Cubature Criterion

Navigation positioning is the premise and basis for the precise strike of guided weapons.Therefore,the interference to the Global Navigation Satellite System(GNSS)is needed to make the enemy 's precision-guided weapons ineffective,that is the “navigation confrontation”,which is a common means adopted by various military powers in future wars.In order to prevent the enemy from interfering with our GNSS positioning signal,it is usually necessary to destroy the navigation interference source or directly avoid the navigation interference source,and this must be based on accurately locating and tracking the navigation interference source.Therefore,it is of great significance to carry out research on tracking and localization of GNSS interference sources.Under the complex tracking environment of the battlefield,the data measured by the direction finding equipment of the interference signal often contains a large amount of interference and noise information.How to extract accurate position information from the observation target data containing interference and noise,and achieve accurate tracking and Positioning has become the main content of this paper.In order to solve the problem of accurate tracking of interference source tracking in battlefield environment,based on the establishment of GNSS interference source tracking model and mechanism,the Cubature Kalman filter(CKF)target tracking algorithm is taken as the research object,and high-precision tracking of interference sources,robust tracking of interference sources and multi-interference source tracking are respectively studied and analyzed.The specific research content and achievements are as follows:(1)The high-precision tracking of interference sources.This paper proposes a Square Root CKF(SRCKF)interference source tracking algorithm and a Backward-Smoothing CKF(BSCKF)interference source tracking algorithm.In the SRCKF algorithm,the square root filtering method is introduced into the CKF filtering framework.In the prediction and updating phase,the state is predicted and updated by using the square root form;In the BSCKF algorithm,the backward smoothing is introduced into the CKF filtering framework.Based on the CKF algorithm,the secondary CKF filtering is implemented by using the backward smoothing function;At the same time,the fading factor correction mechanism is introduced to improve the stability of the interference source detection in the complex environment.The simulation results show that the SRCKF algorithm can effectively avoid the filter divergence and improve the filtering accuracy and system stability to a certain extent.The BSCKF algorithm can effectively reduce the error accumulation caused by the system iteration,thus further improving the filtering precision of the interference source tracking;at the same time,the fading factor mechanism is introduced to improve the accuracy of interference source tracking in complex environment and increase the stability of tracking.(2)The robust tracking of interference sources.This paper proposes a M-Estimation Robust CKF(MR-CKF)interference source tracking algorithm,and a M-Estimation Robust Backward-Smoothing CKF(MR-BSCKF)interference source tracking algorithm.In the MRCKF algorithm,the Constrained Total Least Squares(CTLS)criterion is introduced into the CKF filter framework.First,the Mahalanobis distance is used to judge the outlier data,and then the P-Huber function is used to process the outliers.In the MR-BSCKF algorithm,the backward smoothing idea and the CTLS criterion are combined in the CKF filter framework.Firstly,the Mahalanobis distance and the P-Huber function are used to achieve robust tracking,and then the backward smoothing function is used to implement the second robust CKF filtering.The simulation results show that the MR-CKF algorithm can judge and process the data with outliers and improve the ability to resist the outliers to some extent;the MR-BSCKF algorithm reduces the probability of false positive judgment and the error accumulation caused by system iteration,thus further improving the robust tracking filtering precision of the interference source.The uniformity of filtering accuracy and resistance to outliers is better achieved.(3)The multi-interference source tracking.This paper proposes a Gaussian Mixture PHD based on CKF(CKF-GMPHD)interference source tracking algorithm and a measurementassisted weight correction GM-PHD based on CKF(CKF-MCGMPHD)interference source.In the CKF-GMPHD algorithm,the CKF filtering idea is introduced into the GM-PHD filtering framework,and the CKF method is used to realize the mean and covariance prediction and update of Gaussian term form PHD function.In the CKF-MCGMPHD algorithm,the measurement auxiliary weight correction mechanism is introduced into the GM-PHD filtering framework.Firstly,the measurement auxiliary weight correction strategy is introduced to correct the weight of the Gaussian term,and then the correction term selection mechanism is introduced to determine the Gauss term to be corrected.The simulation results show that the CKF-GMPHD algorithm can avoid the nonlinear motion of the target from affecting the performance of the tracking system,thus further improving the filtering accuracy of multiinterference source tracking;the CKF-MCGMPHD algorithm can avoid the instability of Gaussian term weights and reduce the likelihood calculation error caused by measurement mismatch,thus reducing the computational complexity of the target tracking.