Quaternion ESPRIT parameter estimation method for magnetic dipole pair array

Abstract

A quaternion ESPRIT parameter estimation method for a magnetic dipole pair array comprises the following steps: sampling output signals of an electromagnetic vector sensor array to obtain a first set of sampling data, and synchronously sampling output signals after a delay of delta T to obtain a second set of sampling data; constructing a first received quaternion data matrix, a second received quaternion data matrix and full-array received data; calculating the autocorrelation matrix of the full-array received data, and performing quaternion feature decomposition on the autocorrelation matrix to obtain array steering vector estimated values corresponding to the first set of sampling data and the second set of sampling data and an array steering vector matrix estimated value corresponding to the full data; obtaining the estimated value of the signal arrival angle based on the array steering vector estimated value corresponding to the first set of sampling data; and reconstructing an array steering vector estimated value of an electric dipole sub-array and an array steering vector estimated value of a magnetic dipole sub-array in the Z-axis direction, and obtaining estimated values of signal polarization parameters according to the rotational invariance relation between sub-array steering vectors.

Description

technical field [0001] The invention belongs to the technical field of signal processing, in particular to a parameter estimation method of an electromagnetic vector sensor array. Background technique [0002] The electromagnetic vector sensor array can not only obtain the azimuth parameters and polarization parameters of the incoming wave signal at the same time, but also effectively reveal the orthogonal characteristics of each component of the electromagnetic wave. Some valuable research results have been obtained. However, most of the existing signal processing algorithms of electromagnetic vector sensor arrays apply the classic long vector algorithm of ESPRIT, MUSIC and other subspace classes. The long vector algorithm model uses a complex long Vector representation can correctly estimate the signal parameters, but it destroys the vector structure of the output data of each component, and cannot fully reflect the time-space-polarization three-dimensional characteristic...

AI Technical Summary

Problems solved by technology

However, most of the existing signal processing algorithms of electromagnetic vector sensor arrays apply the classic long vector algorithm of ESPRIT, MUSIC and other subspace classes. The long vector algorithm model uses a complex long Vector representation, although the signal parameters can be correctly estimated, but the vector structure of the output data of each component is destroyed, and the time-space-polarization three-dimensional characteristics of the incident signal cannot be fully reflected.

Images