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108 results about "Beamforming algorithm" patented technology

Algorithm Interpretation. Beamforming: a spatial filtering method, is a signal processing technique used in sensor arrays for directional signal transmission or reception. MUSIC: Multiple Signal Classification. ESPRIT: Estimation of Signal Parameters via Rotational Invariance Technique.

Real-time frequency domain super-resolution direction estimation method and device

The invention provides a real-time frequency domain super-resolution direction estimation method and a device, and the method comprises the following steps: 1) dividing a linear array for obtaining M1sub-arrays; 2) respectively carrying out beamforming on a time-space two-dimensional signal of each sub-array for obtaining an output beam of each sub-array on the scanning direction; 3) carrying outsynthesis treatment on the output beams of various sub-arrays on the scanning direction for obtaining a multi-sub-array synthetic beam; and 4) and obtaining the target direction according to the multi-sub-array synthetic beam obtained by the step 3). The invention provides the real-time frequency domain super-resolution direction estimation device. The method and the device have the following technical effects: (1) the calculation is highly efficient, the speed is fast, the DSP engineering is convenient to realize, and the real-time treatment can be realized; (2) the method and the device areapplicable to broadband noise target direction finding; (3) the high-resolution beam can be obtained; and (4) compared with the frequency domain beamforming algorithm in the prior art, the method andthe device can obtain beam output with even narrow main lobe and even weak strength of a side lobe, thereby obtaining the target direction estimation with higher resolution.
Owner:INST OF ACOUSTICS CHINESE ACAD OF SCI

Digital pre-distortion structure for beam forming system, and control method thereof

The invention provides a digital pre-distortion structure for a beam forming system, and a control method thereof. The control method comprises the following steps: transmitting an input original signal x(n) via an antenna array after processing the original signal via a predistorter, a DAC, an up-conversion module, a phase shifter and a power amplifier; collecting an output signal yp(n) of each power amplifier by a feedback channel in a time sharing manner; synthesizing an equivalent far field signal y(n) of a main beam direction by using a beam forming algorithm according to yp(n); performing DPD training by using an indirect learning structure or a direct learning structure depending on the y(n) and the x(n), and updating the coefficients of the predistorter; and inputting the generatedDPD signal in the system, transmitting the signal via a transmitting channel, and using the transmitted signal as a linear signal of the main beam direction. The digital pre-distortion structure provided by the invention can greatly simplify the transmitter structure, reduce the computing energy consumption, achieves the linearization of the signal of the main beam direction, and can achieve verygood nonlinear performance when the difference of the nonlinear characteristics of each power amplifier is relatively large.
Owner:TSINGHUA UNIV

Multi-beam sounding sonar water body imaging beamforming algorithm

A multi-beam sounding sonar water body imaging beamforming algorithm relates to the field of signal processing. The multi-beam sounding sonar water body imaging beamforming algorithm mainly comprises:compensating for the propagation loss of the acoustic wave according to a time gain curve in each detection sampling time, and obtaining a background noise level of the current detected water area after time averaging; performing near-field focusing beamforming on the signal and estimating the number of sources under the current snapshot sequence number according to the current background noise level; performing covariance matrix estimation on a signal vector with a snapshot number of 1, and obtaining a new pseudo covariance matrix by reconstructing the data covariance matrix after the forward and backward smoothness; performing singular value decomposition on the pseudo-covariance matrix, using conventional beamforming output results and an array manifold to construct a spatial spectralfunction, and obtaining a multi-beam sounding sonar water body imaging result. The algorithm can be widely applied to the multi-beam sounding sonar water body imaging function, can effectively suppress the background noise of the multi-beam sounding sonar water body imaging, and can improve the sonar imaging quality.
Owner:HARBIN ENG UNIV

Double-focusing beamforming method based on self-adaptive weighting

The invention discloses a double-focusing beamforming method based on self-adaptive weighting. An amplitude apodization technology, a virtual array element technology and a self-adaptive weighting method are introduced into an ultrasonic imaging system, and ultrasonic imaging of self-adaptive weighting double-focusing beamforming is realized by two-times delay superposition on the basis of a dynamic focusing technology. The double-focusing beamforming method comprises the following steps of: firstly, extracting effective information of a plurality of superposed sound fields by using the virtual array element technology; secondly, performing fixed-point focusing after performing amplitude apodization on the extracted information during first focusing; and finally, generating self-adaptive weighting coefficients by using a self-adaptive beamforming algorithm according to beamforming signals of the first focusing, and performing self-adaptive weighting beamforming focusing on the beamforming signals of the first focusing again so as to obtain data of finally imaged scanning lines. By the method, the shortcoming that a near-field region has severe pseudomorphism when the traditional double-focusing method based on virtual array elements is implemented is overcome, and an ultrasonic imaging effect is remarkably improved.
Owner:CHONGQING UNIV

Multi-group multicast joint beamforming algorithm design based on intelligent reflecting surface

The invention discloses a multi-group and multicast joint beamforming algorithm design based on an intelligent reflecting surface. Particularly, under the single-mode constraint condition that the maximum transmitting power of the base station and the phase deviation of the IRS reflection unit are given, the method aims at designing a joint optimization strategy and algorithm of base station transmitting beams and IRS reflection phase deviation with fair user service quality. Aiming at the established non-convex and multivariable fractional optimization problem, the method is based on a fractional programming theory and an alternating optimization technology, and adopts a generalized Dinkelbach's algorithm (GDA) to carry out conversion solution. For complex sub-problems in the GDA algorithm, a matrix lifting technology is adopted to be converted into a positive semidefinite programming problem for optimization, and a feasible solution meeting the requirement that the rank is 1 is obtained through a Gaussian randomization technology. Through computer simulation verification and comparison, it is shown that the scheme provided by the invention can significantly improve the received signal to interference plus noise ratio (SINR) of the user with the worst link quality in the network.
Owner:NANTONG UNIVERSITY

Angle domain sparse beam forming algorithm based on 3D-MIMO system noisy channel coefficient

The present invention belongs to the technical field of communication, and especially relates to an angle domain sparse beam forming algorithm based on a 3D-MIMO system noisy channel coefficient. The angle domain sparse beam forming algorithm enables a 3D beam forming problem to be modeled as an optimization model of minimum base station transmitting power by aiming at a single cell MU-MIMO downlink channel system; then converts the original problem into an equivalence optimization problem in the angle domain through angle domain conversion; and adds an l1 norm penalty term of an angle domain pre-code to the beam forming optimization problem by utilizing the angle domain sparse characteristics of a 3D-MIMO channel, so that the obtained angle domain pre-code meets certain sparsity, and is further approximate to the actual solution of the optimal angle domain pre-code, thereby improving system performance; and finally converts the optimization problem into a second-order cone programming problem through variable substitution, and then solves the problem through a convex optimization tool. A simulation result shows that the system performance of the angle domain sparse beam forming algorithm of the present invention is greatly better than the system performance of a non-robustness beam forming algorithm in the scene with big channel noise errors.
Owner:FUDAN UNIV

Three-dimensional passive imaging method and system for brain focusing ultrasonic cavitation real-time monitoring

The invention provides a three-dimensional passive imaging method and system for brain focusing ultrasonic cavitation real-time monitoring. On the basis of calibrating cavitation signal distortion due to skull shielding, array elements of a surface array in two directions are subjected to robust Capon beamforming so as to greatly inhibit the interference from other directions and the mutual action between cavitation microbubbles; on the basis of the differences among phases of cavitation signals, a phase correlation coefficient is introduced for correcting a beamforming algorithm, so that the imaging resolution ratio is improved during the imaging artifact inhibition; and finally, a three-dimensional cavitation body is subjected to processing such as data thresholding and smoothening. The method and the system can solve the problems that the detection sensitivity of a conventional magnetic resonance and active ultrasonic imaging monitoring method on the cavitation is insufficient; the real-time monitoring cannot be realized by the active ultrasonic imaging monitoring; and the performance of a conventional passive imaging method is limited, and the like. A real-time monitoring measure for the cavitation effect in the brain focusing ultrasonic treatment process is provided for the clinics, so that the real-time feedback and control of the grain treatment becomes possible.
Owner:XI AN JIAOTONG UNIV

Wide coverage multi-beam receiving array calibration method

ActiveCN109283511AReduce measurement errorEliminate mechanical rotation lost motion errorWave based measurement systemsData acquisitionEngineering
The invention belongs to the field of multi-beam receiving array calibration, and in particular relates to a wide coverage multi-beam receiving array calibration method. A multi-beam sounding sonar receiver circuit and a transducer array are used as the whole to measure the matrix directivity. After the system is fully warmed up, the influence of the temperature drift of the circuit is eliminated,so that the system is in a daily working state, and it is not necessary to separately measure characteristics of each extension. The measurement process adopts an automatic one-way non-stop rotationmethod, and the data acquisition is controlled by a signal synchronization line, the sampling time is accurately benchmarked, and the mechanical rotation idle running error is excluded. The array calibration is performed by using a near-field focused beamforming algorithm, and an array beam directivity curve and a beam angle error curve are calculated in a small-sized muffler pool. The response capability of the wide-coverage multi-beam receiving array to echoes at various angles can be effectively characterized, the receiving transducer array and the signal conditioning circuit are integrallymeasured; and the method can accurately reflect the overall signal response capability of the receiving system, and can be widely used in the field of multi-beam receiving array calibration.
Owner:HARBIN ENG UNIV

Multicast transmit beamforming method and system based on angle information

InactiveCN102035588AAvoid complete channel information transfer mechanismAvoid transport mechanismSpatial transmit diversitySignal-to-noise ratio (imaging)Communications system
The invention belongs to the technical field of mobile communication, in particular to a multicast transmit beamforming method and system based on angle information. In the invention, the angle information of target mobile stations is utilized to replace the complete channel information used in the traditional beamforming algorithm, thus the method and system are more suitable for a trunking communication system unable to obtain the channel information timely; and the beamforming gain is also utilized to replace the signal to noise ratio (SNR) used in the traditional beamforming algorithm to serve as the new beamforming optimization target, so the specific channel information can not be involved. The flow related to the invention comprises the following three steps: firstly, obtaining the angle information of the mobile stations by a base station; secondly, generating a beamforming vector based on that the beamforming gain serves as the optimization target; and finally, combining the beamforming vector with the signal to finish transmitting. According the invention, the received SNRs of the target users in the group sending services in trunking communication can be obviously improved; and the method and system provided by the invention are very applicable to the trunking communication system.
Owner:BEIJING JIAOTONG UNIV

Audio enhancement method and system

The invention discloses an audio enhancement method. Spatial spectrum of the original multi-channel audio frequency is obtained through a direction of arrival (DOA) estimation algorithm. A plurality of peak values greater than a set threshold value are obtained from the spatial spectrum; and a plurality of estimation direction values of the plurality of peak values are obtained according to the DOA estimation method. A spatial covariance matrix of the plurality of estimation direction values is obtained according to the plurality of estimation direction values and a guiding vector of a microphone array. A CGMM (complex Gaussian mixture model) is initialized and established according to the spatial covariance matrix; and a parameter of the CGMM is updated iteratively by a clustering method.The original multi-channel audio frequency is enhanced by an MVDR (minimum variance distortionless response) beamforming algorithm to obtain an enhanced audio. The method reduces the number of timesthat an EM algorithm iteratively updates the parameter of the CGMM, thereby greatly reducing the amount of calculations. Meanwhile, a category of time-frequency point masking values obtained for eachfrequency band is determined, so that masking values of the same category for each frequency band can be combined together, to solve the problem of fuzzy sequencing.
Owner:AISPEECH CO LTD

Method and apparatus for combined learning using feature enhancement based on deep neural network and modified loss function for speaker recognition robust to noisy environments

Presented are a combined learning method and device using a transformed loss function and feature enhancement based on a deep neural network for speaker recognition that is robust to a noisy environment. The combined learning method using the transformed loss function and the feature enhancement based on the deep neural network for speaker recognition that is robust to the noisy environment, according to an embodiment, may comprise: a preprocessing step for learning to receive, as an input, a speech signal and remove a noise or reverberation component by using at least one of a beamforming algorithm and a dereverberation algorithm using the deep neural network; a speaker embedding step for learning to classify an utterer from the speech signal, from which a noise or reverberation component has been removed, by using a speaker embedding model based on the deep neural network; and a step for, after connecting a deep neural network model included in at least one of the beamforming algorithm and the dereverberation algorithm and the speaker embedding model, for speaker embedding, based on the deep neural network, performing combined learning by using a loss function.
Owner:IUCF HYU (IND UNIV COOP FOUND HANYANG UNIV)
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