4 results about "Hermitian matrix" patented technology

The application relates to a real-valued super-resolution direction estimation method of a high-order acoustic field sensor array, which uses a received signal covariance matrix to approximately calculate an estimated value of noise power, avoids the iterative operation process of noise power in the prior art, improves the calculation efficiency, and reduces the floating-point operation amount. By constructing an augmented matrix, the array receiving data matrix and the array manifold matrix with a multi-dimensional structure of an element are made into Hermitian matrices, so that the unitary transformation processing of the related parameters of the high-order acoustic field sensor array is realized. On the basis of obtaining the array receiving data matrix and the array manifold matrix in the real number domain, the array receiving data matrix and the array manifold matrix are applied to a sparse approximate minimum variance method with a variable exponential factor, and the completely real-valued sparse approximate minimum variance direction estimation with a variable exponential factor is realized.

The invention discloses a digital signal processing method, a direction of arrival estimation method, a direction of arrival estimation device, an integrated circuit, a device and equipment, an M * M Toeplitz-Hermite matrix RM is constructed based on a digital signal obtained by processing a system receiving signal, the RM is inversed, and then the digital signal processing function is realized based on continuous processing, a B * B sub-matrix in a block matrix of an RM is taken as a basic calculation unit according to a Gauberg-Schelitscher decomposition expression, and a matrix multiplication and addition operation of B parallelism is executed through hardware to obtain that B is greater than or equal to 2, and M is an integral multiple of B. According to the embodiment of the invention, the operation parallelism degree is higher, and the operation speed is higher.

The invention particularly relates to a real-valued super-resolution azimuth estimation method for a high-order sound field sensor array, which approximately calculates an estimated value of noise power by using a received signal covariance matrix, avoids the process of iterative operation of the noise power in the existing method, improves the calculation efficiency and reduces the floating point calculation amount. By constructing an augmented matrix, an array receiving data matrix and an array manifold matrix of which array elements have a multi-dimensional structure become a Hermitian matrix, so that unitary transformation processing of related parameters of a high-order sound field sensor array is realized. On the basis of obtaining an array receiving signal data matrix and an array manifold matrix of a real number field, the array receiving signal data matrix and the array manifold matrix are applied to a sparse approximate minimum variance method of a variable exponential factor, and completely real-valued variable exponential factor sparse approximate minimum variance orientation estimation is realized.

This invention discloses a method for calculating polarization parameters of radar array signals based on the vector MUSIC algorithm, relating to the field of radar array signal processing technology. First, this invention obtains the polarization signal steering vector matrix and noise subspace corresponding to the radar array signal, and performs matrix multiplication to obtain a 2×2 complex Hermitian matrix. By extracting the ratio of the imaginary to the real part of the second element in the first row of the complex Hermitian matrix, the arctangent function is directly applied to obtain the polarization phase difference, avoiding the process of solving for eigenvectors. Then, the minimum eigenvalue of the complex Hermitian matrix is ​​calculated, and combined with the trace of the complex Hermitian matrix, the arctangent value is calculated, and the polarization auxiliary angle is directly derived, eliminating the need for a complete eigenvalue decomposition process. Therefore, when calculating polarization parameters, the calculation starts from the definition of polarization parameters, i.e., directly from the definition of polarization phase difference and polarization auxiliary angle, directly avoiding the complex calculations brought about by eigenvalue decomposition, thereby reducing computational complexity and improving computational accuracy.