Multi-objective Parameter Estimation Of Polarization Sensitive Array

Due to the superior system performance of polarization-sensitive arrays,they have excellent value and broad application prospects in the military and civilian electronics fields.In the field of polarization-sensitive array signal processing,scholars usually apply parameter estimation methods that are directly applicable to scalar arrays directly to vector arrays,so that the orthogonal characteristics of polarization-sensitive arrays are not fully utilized.This paper will study the multi-parameter estimation algorithms of polarization-sensitive arrays,and explore the parameter estimation of coprime arrays,Lshaped arrays,and uniform circular arrays.Aiming at the parameter estimation of the uniform linear array polarization sensitive array,For the parameter estimation of the uniform linear array polarization sensitive array,scholars have given a joint parameter estimation algorithm based on the quaternion model.Simulation experiments show that the Q-MUSIC algorithm can well retain the orthogonal characteristics of the polarization sensitive array,and the algorithm has higher accuracy.For Lv-MUSIC algorithm and polarized ESPRIT algorithm.on the basis of.Compressed sensing theory is introduced into the parameter estimation algorithm,and the space-ofarrival angle information is obtained by the-norm decomposition of the received signal's covariance matrix and cross-covariance matrix,and then the received vector is sparsely reconstructed to obtain an estimation of polarization parameters.Finally,experimental simulations verify the compression-based The performance of the perceptual estimation algorithm is better than the Lv-MUSIC algorithm and the polarized ESPRIT algorithm.In order to improve the utilization efficiency of array elements,the polarizationsensitive array of coprime array is introduced to study the parameter estimation algorithm.The number of elements in the virtual array is greater than the number of physical elements.Since the tri-orthogonal dipole array can obtain the received signal information in three dimensions,the dimension of the reconstruction of the covariance matrix is greater than the number of signal sources,so Three methods based on spatial smoothing,Toeplitz matrix reconstruction,and compressed sensing are studied.When experimental conditions are set when the number of incident signal sources is greater than the number of physical elements and less than the number of virtual elements,experimental simulations prove that all three algorithms can effectively estimate parameters..In the research of polarization-sensitive arrays,the introduction of polarization information can obtain more information,and at the same time,it also brings the problem of excessive algorithm computation and complexity.The dimensionality reduction MUSIC algorithm mainly separates the polarization information and the space angle of arrival information,and estimates them by the MUSIC algorithm.For the L-type polarization-sensitive array,For the L-shaped polarization-sensitive array,the airspace information is stripped out first,and then solved by the Root-MUSIC algorithm.,and then the modulus parameters are used to solve the polarization parameters through the optimization function.The Uniform Circular Array(UCA)can provide 360 ° coverage on the azimuth plane and has uniform performance regardless of the angle of arrival.In view of this characteristic,the space and polarization domain information are separated in the beam space,and the space arrival angle information is obtained.Then,an optimization function is established to solve the polarization parameters through the constraint condition of the modulus.Finally,the performance and calculation amount of the dimensionality reduction parameter estimation algorithm are compared through experimental simulation.