10 results about "Fourth order cumulants" patented technology

This invention belongs to the field of sound source localization technology and discloses a near-field sound source localization method based on a symmetrical nested array and weighted fourth-order cumulants. The invention uses a centrally symmetrical nested array as the signal receiving structure. By constructing a weighted fourth-order cumulant matrix, it improves the stability of statistical quantities while suppressing Gaussian noise, and establishes an equivalent high-order statistical model based on the array geometry. In the parameter estimation stage, a two-stage one-dimensional estimation strategy decoupling angle and distance is adopted: first, one-dimensional subspace estimation of angle is performed based on the weighted fourth-order cumulant matrix; then, one-dimensional search estimation of distance is performed under angle constraints, thereby avoiding the traditional two-dimensional spectral peak search process in the near field. Without increasing the array hardware complexity, this invention improves localization accuracy and stability through high-order statistical optimization and parameter estimation process improvement, while significantly reducing computational complexity, and has good engineering application value.

This invention belongs to the field of electromagnetic interference detection technology for the BeiDou Navigation Satellite System, and discloses a method, system, device, and medium for identifying BeiDou multi-domain characteristic interference signals. This invention constructs a two-stage step-by-step identification strategy combining time-frequency maps and fourth-order cumulant maps. In each stage, an interference signal identification model based on a lightweight multi-scale network is built. In the first stage, based on the time-frequency map and the interference signal identification model, highly distinguishable interference types are identified. For easily confused interference signals that cannot be identified in the first stage, the second stage, based on the fourth-order cumulant map and the interference signal identification model, improves the accuracy of identifying easily confused interference types, ultimately improving the overall identification accuracy for complex interference. Simultaneously, the introduction of the lightweight multi-scale network effectively solves the problems of insufficient key feature capture and high computational cost of multi-scale structures in existing neural network technologies.

The application discloses a vehicle positioning method and device based on a spatial spectrum estimation method, and the method comprises the following steps: receiving positioning signals of vehicles to be positioned by using multiple coprime antenna arrays and obtaining first received signals by considering noise interference; calculating fourth-order cumulant matrices of the first received signals, constructing a virtual matrix, eliminating Gaussian noise, vectorizing the fourth-order cumulant matrices to obtain second received signals, and calculating a position matrix of a virtual array according to positions of antennas in the coprime antenna array; performing spatial smoothing processing on the second received signals corresponding to a uniform linear part of the virtual array to obtain a smoothing matrix; when the number of vehicles to be positioned is unknown, constructing a noise subspace according to the smoothing matrix and a hyperparameter; constructing a spatial spectrum function according to the noise subspace and a direction vector and estimating a wave arrival direction; and determining vehicle positions in combination with position information of the array. The application can simultaneously position multiple vehicles in real time and with high precision under the condition that the number of vehicles is unknown, and can meet actual application requirements.

The invention discloses a monitoring method for a low-altitude target in a multi-signal environment, which comprises the following steps of: constructing a plurality of receiving nodes or receiving stations on a foundation or a space-based to monitor an aerial target, uniformly distributing the plurality of receiving stations as far as possible, and establishing a data transmission link among the plurality of stations to form multi-station cooperative monitoring and receiving; four stations are adopted in a monitoring area, and multiple stations adopt respective independent channels to receive, convert frequency and sample space radiation signals; taking any one of the plurality of receiving observation stations as a main station, and transmitting signals received by other receiving stations to the main station to form multiple paths of observation signals; after fourth-order cumulant matrix calculation and eigenvalue decomposition processing are carried out on a received signal vector array after convergence of a master station is completed, an interference filtering matrix is formed and obtained, and thus monitored aerial target signals are accurately obtained; through the obtained aerial target monitoring signal, the time difference between the arrival of the aerial target monitoring signal and the arrival of the main station is obtained, and the aerial target position is calculated through the time difference.

The application discloses a blood flow parameter determination and display method and device, an electronic device and a computer readable storage medium. The blood flow parameter determination method comprises the following steps: acquiring echo data of multiple pulses of wall-filtered pixel points in an imaging region; constructing four sub-sequences based on the echo data, and calculating fourth-order cumulants of the echo data based on the four sub-sequences; and calculating the blood flow parameter based on the echo data and the fourth-order cumulants. In the application, the four sub-sequences are constructed based on the echo data of multiple pulses of wall-filtered pixel points in the imaging region, the fourth-order cumulants of the echo data are calculated by using the constructed sub-sequences, and the problem that the autocorrelation method is affected by the accuracy of the blood flow parameter estimation in the additive Gaussian color noise and multiplicative noise background is eliminated by using the characteristics that the fourth-order cumulants are not sensitive to the additive Gaussian color noise and multiplicative noise, so that the accuracy of the blood flow parameter estimation is improved.

This invention discloses a high-degree-of-freedom, low-coupling sparse array design method based on fourth-order cumulants, comprising: A: constructing a non-Gaussian signal receiving model and constructing a fourth-order differential array of the non-Gaussian signal using fourth-order cumulants; B: constructing a coprime displacement array composed of two coprime displacement subarrays and determining the array parameters; C: constructing an expanded nested array composed of two nested subarrays and determining the array parameters; D: constructing an expanded nested coprime displacement array based on the coprime displacement array and the expanded nested array and determining the expansion factor, ultimately obtaining a high-degree-of-freedom, low-coupling sparse array based on fourth-order cumulants. This invention can achieve stronger anti-coupling capability while maintaining a degree of freedom comparable to existing sparse arrays, and can significantly improve the signal estimation accuracy and direction-of-arrival resolution.

This invention discloses a deep learning-based DOA estimation method, comprising the following steps: S1: Constructing an array data model using received array data, and obtaining the output signal X from the array data model; S2: Constructing the fourth-order cumulant of the output signal X, and obtaining the fourth-order cumulant matrix C of the output signal X. Y S3: For C Y After vectorization and deduplication of the results, the covariance matrix is ​​calculated using a spatial smoothing algorithm. S4: The calculated covariance matrix is ​​input into a trained CNN model to obtain the DOA estimation result. This invention combines sparse arrays, spatial smoothing algorithms, and deep neural network models to locate interference signals. This effectively improves the accuracy of the DOA estimation algorithm while reducing computational load, hardware costs, and computational burden.

This invention provides a signal azimuth estimation method and device based on high-order cumulants and auxiliary array elements, comprising: S1: receiving N far-field signals from M spatial sensor arrays, and obtaining the array manifold formed by the sensor arrays for the N signals; S2: performing Fourier transform on the received array signals to obtain a signal model; S3: expanding the array based on the signal model to obtain the fourth-order cumulants of the received signals in the model; S4: obtaining the direction vector of the expanded signal, and generating a construction matrix of the fourth-order cumulants based on the Kronecker product, and transforming the matrix; S5: performing eigenvalue decomposition on the fourth-order cumulants to obtain a noise subspace, and adjusting the row order of the noise subspace according to the element order of the array vectors to obtain an adjusted noise subspace, and estimating the source azimuth angle according to the subspace orthogonality principle. This solves the problem of correcting azimuth-dependent array errors.

The invention discloses a fourth-order layered array design method based on a fourth-order cumulant, and the method comprises the steps: constructing a first sub-array, and generating a second-order sum common array and a second-order difference common array based on the first sub-array; judging whether the first sub-array meets the condition that the second-order sum common array and the second-order difference common array have no hole, if so, entering the next step, and if not, returning to reconstruct the first sub-array; generating a second sub-array based on the second order and the common array; according to the first sub-array and the second sub-array, calculating to obtain two three-order difference common arrays; generating a third sub-array based on the two three-order difference common arrays; and obtaining a union set of the first sub-array, the second sub-array and the third sub-array to obtain a fourth-order layered array. The degree of freedom of the obtained array structure can be enhanced, and the mutual coupling effect between the sensors can be reduced.

The present application relates to electronic communication signal positioning identification technical field, especially to a kind of hybrid field source signal positioning method and system based on meshless, for the target area in hybrid field source, construct four-order cumulant matrix that has eliminated distance parameter: generate four-order cumulant matrix equivalent to uniform array by interpolation operation on four-order cumulant matrix;Based on four-order cumulant matrix equivalent to uniform array and using atomic norm theory to construct hybrid field source direction of arrival estimation optimization problem;Hybrid field source direction of arrival estimation optimization problem is solved using the MUSIC matrix characteristic space decomposition method, and direction of arrival estimation value and distance estimation value of hybrid field source are obtained according to solving structure.The present application can realize high-precision hybrid field positioning, and positioning success rate is high, with good robustness, with good application prospect.