9 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 DC power system fault detection method, system and device, and a storage medium, and relates to the technical field of fault detection. The method comprises the following steps: collecting node data in a direct-current power system, and generating a column vector according to a time sequence; performing standardization processing on the column vector to obtain a standard non-Hermitian matrix; calculating a standard matrix product and a corresponding characteristic value of the standard non-Hermitian matrix, and determining characteristic value distribution of the standard matrix product according to a single-ring theorem; the characteristic value distribution is limited by the inner ring radius and the outer ring radius; constructing a random variable based on the characteristic value distribution and the average spectral radius; when the random variable is smaller than the radius of the inner ring, judging that the system breaks down; and when the random variable is greater than or equal to the radius of the inner ring, determining that the system has no fault. According to the method, the defects of the traditional method in quickness, anti-interference performance and multi-end system adaptability can be overcome by quantifying the linear change trend of the fault correlation characteristics.

The invention discloses a radar array signal polarization parameter resolving method based on a vector MUSIC algorithm, and relates to the technical field of radar array signal processing.The radar array signal polarization parameter resolving method comprises the steps that firstly, a polarization signal steering vector matrix and a noise subspace corresponding to a radar array signal are obtained, matrix multiplication is conducted, and a 2 * 2 complex ermitt matrix is obtained; by extracting the ratio of the imaginary part to the real part of the second element in the first row in the complex Ermit matrix, the polarization phase difference is obtained by directly applying the arc tangent function, and the solving process of the feature vector is avoided; then the minimum eigenvalue of the complex Ermit matrix is calculated, an arc tangent value is calculated in combination with the trace of the complex Ermit matrix, and the polarization auxiliary angle is directly derived, so that a complete eigendecomposition process is omitted, and therefore, when the polarization parameters are calculated, the polarization auxiliary angle is calculated from the angle of definition of the polarization parameters; in other words, resolving is directly carried out from the angles defined by the polarization phase difference and the polarization auxiliary angle, and complex operation caused by characteristic decomposition is directly avoided, so that the calculation complexity is reduced, and the calculation precision is improved.

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.

This invention provides a secure data sharing and exchange method and system, relating to the field of data processing technology. The method includes: acquiring data to be shared; generating a seed vector through a data sharer and sharing the seed vector with a data consumer; generating a time-varying access state vector for implicit decoding in the data consumer based on the seed vector and a random Hermitian matrix; generating a fuzzy state vector about the data to be shared in the data sharer using orthogonal projection technology based on the time-varying access state vector; sending the fuzzy state vector to the data consumer; and performing dot-product implicit decoding on the fuzzy state vector in the data consumer using the pre-stored time-varying access state vector to complete the secure sharing of the data to be shared. The entire process, combining orthogonal projection technology and time-varying access state vectors, not only ensures the security of data sharing but also guarantees efficient and real-time data exchange.

Various aspects of the present disclosure generally relate to wireless communication. Various aspects relate generally to determining information metrics associated with an eigenvalue decomposition (EVD) operation associated with a four by four Hermitian matrix. Some aspects more specifically relate to determining a scalar (e.g., an eigenvalue or an approximation of an eigenvalue) of the four by four Hermitian matrix and determining a shifted matrix by subtracting a product of the scalar and an identity matrix from the four by four Hermitian matrix. A QR decomposition may be performed on the shifted matrix to determine first derived information, representable by a three by three Hermitian matrix. An eigenvalue decomposition operation may be performed on the three by three Hermitian matrix to determine the eigenvalues and eigenvectors of the four by four Hermitian matrix.

The invention provides a Hermitian matrix eigenvalue solving method and an FPGA (Field Programmable Gate Array) solving system thereof, relates to the technical field of signal processing, and solves the problem of how to make up defects of a current solving method from multiple aspects such as precision, convergence speed, eigenvalue generation and resource consumption. The method comprises the following steps: constructing m orthogonal bases which are mutually orthogonal from m columns of a Hermitian matrix, combining the m orthogonal bases into an eigenvalue matrix Q and an eigenvector matrix R, and multiplying the eigenvalue matrix Q and the eigenvector matrix R to obtain a new Hermitian matrix; through multiple rounds of iteration, the matrix Q and the matrix R obtained through the last iteration are solving results; the input data in the solving process adopts a double-precision floating-point number form and is kept; during solving, an accumulator is adopted to calculate a complex matrix multiplication result; when the matrixes are multiplied, each matrix element outputs data in a real part and imaginary part alternation mode, and a result is obtained after alternation multiplication is carried out by a plurality of floating-point complex multipliers. According to the method, a high-precision and rapid Hermitian matrix eigenvalue decomposition effect can be realized under the condition of low FPGA (Field Programmable Gate Array) resource consumption.

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.