Research On DOA Dynamic Estimation And Tracking Algorithm In High-speed Railway Scenario

With the high-speed railway technology developing in recent years,the construction of high-speed railways around the world is growing rapidly.Due to the open electromagnetic environment in which the high-speed rail operates,the air interface is easily affected by interference signals.The electromagnetic environment around the railway is increasingly complex with the construction around the railway,so railway mobile communication system is interfered frequently.Therefore,it is important to detect interference in railway mobile communication system.Due to lack of research on interference direction finding algorithms suitable for high-speed dynamic railway scenarios,it is significant to carry out real-time tracking of direction of arrival(DOA)signals in dynamic railway scenarios.In this thesis,the research on DOA tracking algorithm is carried out in high-speed railway scenarios.The main work is as follows:(1)Due to the multipath effect,the signals received by the array are mostly coherent signals.The covariance matrix of the received data cannot be properly decomposed into a signal subspace and a noise subspace.In addition,in a single RF chain antenna array,the received signals at the antennas are not sent to the receiver directly,and spatial covariance matrix,which is essential in Multiple Signal Classification(MUSIC)algorithm,is thus unavailable.In this thesis,with the construction of a decoherent subarray and reconstruction of the covariance matrix,the MUSIC algorithm is successfully applied to the DOA estimation of coherent sources in a single RF chain antenna array.Simulation results indicate this algorithm outperforms the previous algorithms on accuracy of DOA estimation for coherent sources in single RF chain circular array.(2)In high-speed railway dynamic scenarios,the angle of the signal received by the array changes rapidly with time,therefore the previous DOA estimation methods are not suitable for DOA tracking of high-speed railway systems due to the huge computational burden.In this thesis,Orthogonal Projection Approximation Subspace Tracking of deflation(OPASTd)algorithm based on Kalman filter is built by the combination of the advantages of the Kalman filter and OPASTd algorithm.The proposed algorithm performs data association while estimating the angle,and allows accurate DOA tracking in dynamic scenarios.The simulation results demonstrate that the proposed algorithm is efficient in DOA tracking compared with OPASTd algorithm.