148 results about "Blind signal separation" patented technology

An acoustic device is provided with first and second one or more acoustic elements to generate a first signal that includes mostly undesired audio and substantially void of desired audio, and a second signal that includes desired as well undesired audio respectively. The first one or more acoustic elements are designed and arranged to generate a Cardioid beam with a null at an originating direction of the desired audio. The second one or more acoustic elements are designed and arranged to generate a complementary beam that includes the desired audio. A system is provided with an appropriate signal processing logic to recover the desired audio using the first and second signals. The signal processing logic may practice echo cancellation like techniques or blind signal separation techniques.

The invention discloses a communication receiver anti-interference method on the basis of blind signal separation and a system thereof, wherein the method comprises: utilizing a positive-negative 45 degree dual polarization antenna module to receive signals, changing a two way high frequency signal into an intermediate frequency analog signal, outputting an intermediate frequency digital signal by the intermediate frequency analog signal through analogue/digital conversion and after an orthogonal transformation module, then, carrying out digital down conversion, abstracting, filtering, and the like, transforming into a DSP baseband complex signal, obtaining a statistical independent signal after two ways are separated by the complex signal after passing through a linear blind signal separation algorithm module, carrying out self-adaptive equalization processing to a two way estimating signal after blind separation, counteracting crosstalk in a communication channel, utilizing a known modulation and demodulation method to demodulate a two way independent signal, analyzing a two way signal, and abstracting a useful signal. The invention does not need to use any other reference signals, adopts the linear blind signal separation algorithm to estimate the two way independent separation signal, the calculation complexity is low, and the disturbance to the communication receiver under the condition of low signal-to-noise ratio can be eliminated.

The present invention provides a method for recovering target speech based on shapes of amplitude distributions of split spectra obtained by use of blind signal separation. This method includes: a first step of receiving target speech emitted from a sound source and a noise emitted from another sound source and forming mixed signals of the target speech and the noise at a first microphone and at a second microphone; a second step of performing the Fourier transform of the mixed signals from the time domain to the frequency domain, decomposing the mixed signals into two separated signals U₁ and U₂ by use of the Independent Component Analysis, and, based on transmission path characteristics of the four different paths from the two sound sources to the first and second microphones, generating the split spectra v₁₁, v₁₂, v₂₁ and v₂₂ from the separated signals U₁ and U₂; and a third step of extracting estimated spectra Z* corresponding to the target speech to generate a recovered spectrum group of the target speech, wherein the split spectra v₁₁, v₁₂, v₂₁, and v₂₂ are analyzed by applying criteria based on the shape of the amplitude distribution of each of the split spectra v₁₁, v₁₂, v₂₁, and v₂₂, and performing the inverse Fourier transform of the recovered spectrum group from the frequency domain to the time domain to recover the target speech.

A method of separating and localizing sources of traveling waves, by obtaining linearly independent time-differentiated instantaneous observations of the sources, from spatial derivatives of the traveling wave acquired using a distributed sensor or a sensor array. The sources are blindly separated by direct application of (static) independent component analysis on the time-differentiated observations, yielding both the sources and their direction cosines relative to the sensor geometry. The method is suited for arrays of small aperture, with dimensions shorter than the coherence length of the waves. In one preferred embodiment, three sources are separated and localized from differential observations on four coplanar sensors positioned on the corners of a square. Applications include miniature microphone arrays for intelligent hearing aids with adaptive suppression of interfering signals and nonstationary noise, and integrated RF arrays for resolving or localizing multiple users or channels from a base station or mobile unit.

The invention provides a Doppler fetal cardiac sound instantaneous heart rate detecting method based on blind separation. The detection method comprises the following steps that the Doppler fetal cardiac sound is subjected to denoising preprocessing through a low-pass filter; the processed ultrasonic Doppler fetal cardiac sound is subjected to time-frequency analysis, and the short-time Fourier transform is utilized for solving the time-frequency diagram of the ultrasonic Doppler fetal cardiac sound signals; several characteristic frequency bands are selected from the time-frequency diagram according to the priori knowledge of the periodic characteristic of the time-frequency diagram of the fetal cardiac sound signals, then, different selected frequency band signals are subjected to Fast ICA analysis, independent components are worked out, then, the independent component IC which is most matched with the fetal cardiac sound signals is determined, next, a self-correlation function of the most matched independent component IC is calculated, the peak value is detected, and finally, the fetal cardiac sound heart rate is calculated. The Doppler fetal cardiac sound instantaneous heart rate detecting method is used for calculating the instantaneous heart rate on the clinically collected ultrasonic Doppler fetal cardiac sound signals and has the advantages that the method is simple, the calculation speed is high, the flexibility is good, and the accuracy is high.

The invention relates to an anti-collision algorithm for a frame time slot ultrahigh frequency RFID system based on blind separation. The anti-collision algorithm for the RFID system is put forward based on ICA and frame time slots according to ultrahigh-frequency radio-frequency signals ranging from 860MHz to 960MHz. According to the anti-collision algorithm, steps of an ICA algorithm in the RFID system are analyzed, an antenna system model with blind sources separated is established, the quantity of tags in each time slot is not larger than the quantity of antenna of a reader by means of specific selection of the quantity of the frame time slots, conditions of blind source separation identification tags are met, and synchronous identification can be performed on multiple tags through the ICA algorithm. A simulation result shows that compared with a traditional tag anti-collision algorithm and a blind separation and dynamic bit-slot grouping algorithm, the tag identification rate is high, identification time of the algorithm is less, and feasibility and high efficiency of the blind source separation technology applied to tag identification are verified further. The anti-collision algorithm has potential application value in the engineering field needing high efficiency and intelligent management.

The invention discloses a method for eliminating myoelectricity noise in an electroencephalogram signal based on a single channel. The method is characterized by comprising the steps that firstly, a single-channel electroencephalogram signal is decomposed into a plurality of intrinsic mode components through general average empirical mode decomposition; secondly, blind signal separation s conducted on the intrinsic mode components through multi-set canonical correlation analysis, and a plurality of canonical variables are obtained; finally, the canonical variables with the autocorrelation coefficient lower than a certain threshold value are judged to be myoelectricity noise, the myoelectricity noise variables are removed, and reconstruction is conducted to obtain the electroencephalogram signal with the myoelectricity noise removed. According to the method, the purpose of eliminating the myoelectricity noise in the electroencephalogram signal is effectively achieved from the brand new angle of the single channel, and compared with the traditional blind signal separation technology based on the multiple channels, the myoelectricity noise can be better eliminated. The method is suitable for portable and wearable single-channel and few-channel electroencephalogram devices, is also suitable for multi-channel electroencephalogram devices for clinical diagnosis and neuroscience researches, and significant importance is achieved in further researches of the true physiological activities of the human brain.

The invention discloses a convolutive blind signal separation method based on multi-target optimization joint block diagonalization, mainly solving the problem that in the prior art, not all source signals can be accurately separated from convolutive aliasing signals. The method comprises the steps of: (1) obtaining observation data; (2) calculating the second-order delay correlation matrix of the observation data; (3) constructing a block diagonalization matrix and dividing into submatrixes; (4) establishing a multi-target optimization model about the block diagonalization matrix; (5) estimating the block diagonalization matrix in dependence on the multi-target optimization model; (6) determining whether the difference absolute value between twice block diagonalization matrix evaluated errors is larger than an iteration termination threshold, and if yes, outputting the block diagonalization matrix, and if no, returning to the step (5); and (7) utilizing the block diagonalization matrix to separate source signals from observation signals. The method can accurately separate all source signals from convolutive aliasing signals, has the characteristics of low complexity and high separation efficiency, and can be used for processing voice signals and communication signals.

The invention discloses a power quality signal detection device based on compressed sensing blind signal separation technology. The device comprises a signal conditioning module, which is used for adopting a voltage transformer or a current transformer to connect with a power grid, enabling a power quality signal in an electric system to be input and carrying out filtering through an anti-aliasing low-pass filter to obtain an interference high-frequency signal; a signal sampling module, which is used for carrying out multi-channel synchronous sampling conversion on m-path observation signals and enabling the signals to be input to a signal processing and analysis module in a digital input manner; and the signal processing and analysis module, which is used for carrying out Fourier transform when the digital signals are input to the signal processing and analysis module having an ARM management control function and a DSP calculation and analysis function to obtain complex matrixes, extracting spectrum amplitudes of the two paths of observation signals in the complex matrixes to form a spectrum scatter diagram, determining the number of source signals according to line direction, estimating a hybrid matrix through a clustering method, reconstructing each source signal through a CoSaMP algorithm, and storing the results and outputting and displaying the estimated value of the number of source signals, the estimated hybrid matrix and the separated source signals.

The invention provides a frequency domain convolution blind signal separation method based on multi-objective optimization, which is used for solving the problem that convergence to regressive solution easily occurs in the prior art, and can realize the frequency domain convolution blind signal separation with the number of source signals being smaller than that of observation signals. The methodcomprises the following implementation steps: acquiring a target matrix set R; constructing a diagonalizable matrix B (omega k); constructing a non-orthogonal joint diagonalizable multi-objective optimization model; by utilizing the non-orthogonal joint diagonalizable multi-objective optimization model, estimating the disjunct matrix W (omega k) on each frequency point of the target matrix set R;and acquiring the estimated value of time domain source signals. The method is high in reliability and wide in application range, and can be applied to blind separation of the convolution mixed signals including voice signals, communication signals and the like under over-determined conditions.

The invention discloses a permanent magnet synchronous motor low-speed domain rotor position identification method, so as to solve the problems that the traditional pulse signal injection method usesmultiple filters, the hysteresis effects are superimposed, the dynamic performance of the system is reduced, and the position estimation error is increased. A new low-speed position sensorless controlstrategy is proposed. In particular, on the basis of injection of a high-frequency pulse square wave voltage signal to a two-phase stationary coordinate system, through a generalized second-order integrator (SOGI), current in a beta axis is subjected to signal separation and amplitude demodulation, two paths of orthogonal signals including rotor position information are obtained, and through heterodyne and a rotor position observer, the rotor position information is extracted, and no filter is used during the whole signal demodulation process. The dynamic performance of the control system andthe accuracy of the rotor position estimation are effectively improved.

A communications device for separating source signals provided by M signal sources includes an antenna array comprising N antenna elements for receiving at least N different summations of the M source signals. At least two of the N antenna elements are correlated and have different polarizations for receiving at least two of the N different summations of the M source signals, with N and M being greater than 1. A receiver is connected to the antenna array for receiving the at least N different summations of the M source signals. A blind signal separation processor is connected to the receiver for forming a mixing matrix comprising the at least N different summations of the M source signals, and for separating desired source signals from the mixing matrix. The mixing matrix has a rank equal to at least N.

A communications device for separating source signals provided by M signal sources includes an antenna array comprising N correlated antenna elements for receiving at least N different summations of the M source signals, with N and M being greater than 1. A receiver is connected to the antenna array for receiving the at least N different summations of the M source signals. A blind signal separation processor is connected to the receiver for forming a mixing matrix comprising up to the at least N different summations of the M source signals, and for separating desired source signals from the mixing matrix. The mixing matrix has a rank equal to at least N.

The invention provides a signal blind detection method based on an artificial fish swarm algorithm. According to the method, a basic model of artificial fishes and a model of each behavior are constructed by adopting a bottom-top design method, the quadratic planning performance function of blind signal detection is solved by using the model, and the influences of each parameter in the artificial fish swarm algorithm, system environment conditions and the like are researched and analyzed according to the flow deduction implementation step of a basic fish swarm optimization algorithm. As indicated by a simulation result, the artificial fish swarm algorithm is a very effective method for application to signal blind detection.

The invention provides a combined classifier multi-signal modulation identification method based on fractional Fourier transform. The method includes steps: firstly, solving the problem of multi-signal separation by employing an FRFT technology, then realizing accurate classification and identification of a strong signal with the combination of a SVM by employing a pre-trained CNN in time and frequency domains, extracting characteristics including the spectrum kurtosis, a root-mean-square value, an AR characteristic coefficient and a Renyi entropy etc. of a weak signal to realize characteristic fusion, performing dimension reduction on fusion characteristics by employing a principal component analysis (PCA) to improve the system validity, and inputting the characteristics after dimension reduction to the SVM to realize second signal classification and identification and finally realize accurate and rapid identification of the multi-signal modulation mode with low signal to noise ratio.