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Quaternion neural network based near-field source parameter estimation method for 3d polarized array

ActiveCN122362321BReduce training cost growth issuesSolving estimation challenges
The application belongs to the technical field of array signal processing, and relates to a three-dimensional polarized array near-field source parameter estimation method based on a quaternion neural network, which comprises three-dimensional polarized array near-field source receiving signal modeling, establishment of a near-field receiving signal model of a three-dimensional co-point orthogonal magnetic loop dipole array, and coupling of receiving direction vectors of the three-dimensional COLD array with three-dimensional space parameters and two-dimensional polarization domain parameters; network input data is constructed; training set data is constructed; a quaternion neural network architecture is constructed and trained. The application uses the quaternion neural network to realize space-polarization domain parameter estimation of a near-field source in the three-dimensional COLD array, extracts multi-dimensional signal characteristics of the three-dimensional COLD array by using the quaternion neural network, and decouples space and polarization domain parameters by constructing polarization coherent and non-polarization coherent two-way quaternion covariance matrix data, so that the training cost growth problem caused by sample combination in multi-dimensional parameter estimation is greatly reduced.
Owner:XIDIAN UNIV HANGZHOU RES INST +1