The Joint Polarization-angle Estimation Method Of Conformal Vector Array Based On Spatial Sparse Characteristics

The vector conformal array is an antenna array in which several electromagnetic vector sensors are laid on the surface of the carrier and are conformal to the carrier.Compared with the traditional scalar sensor array,which can only obtain the spatial information of the target,the vector conformal array can obtain the spatial and polarization domain information of the target at the same time,and has stronger advantages in information acquisition and target detection.In addition,because the array can be integrated with the shape of the carrier,it has the advantages of light weight and strong concealment,so it has a wide range of applications in various fields.This paper takes the polarization-angle parameter joint estimation of vector conformal arrays as the research background,aiming at the problem of performance degradation of traditional joint parameter estimation algorithms under low signal-to-noise ratio and small number of snapshots,the starting point is the spatial sparse characteristic of the incident signal.,mainly completed the following research contents:Firstly,the basic theoretical knowledge of polarization and vector conformal arrays of electromagnetic waves are studied.This paper studies the basic concept and physical meaning of electromagnetic wave polarization,and classifies its polarization modes.Secondly,it introduces the mathematical modeling of the spatial steering vector of the vector sensor array.Finally,it introduces how to use the Euler rotation transform to obtain the vector common The orientation of the array elements in the conformal array,and the signal receiving model of the vector conformal array.Secondly,in order to solve the problem that the performance of the traditional polarization-direction of arrival(DOA)parameter estimation algorithm based on subspace class deteriorates sharply under the condition of low signal-to-noise ratio and small number of snapshots,this paper uses the sparseness of the incident signal.This paper proposes a polarization-angle parameter estimation algorithm based on Sparse Bayesian Learning(SBL).The algorithm establishes a sparse representation of the signal model by dividing the spatial domain into a grid,then derives the posterior probability of the signal by Bayesian estimation,and completes the estimation of the incident signal by updating the parameters.The performance of the proposed algorithm is compared with Multiple Signal Classification(MUSIC)and tensor-based MUSIC(Tensor-MUSIC),and the results show that the proposed algorithm has higher estimation accuracy and resolution under low SNR and small snapshots.Finally,in order to reduce the error caused by the polarization-angle parameter estimation algorithm based on SBL in spatial grid division,this paper proposes a polarization-angle parameter estimation algorithm based on SBL under the condition of grid mismatch.The off-grid model of the signal was established,and the error of the array spatial guidance vector of the off-grid model was estimated by first-order Taylor expansion,and then the incident signal was estimated by sparse Bayes learning algorithm and iteratively updating the grid parameters.The accuracy of the proposed algorithm is verified by comparing with the matching tracking algorithm.