32 results about "Channel complex" patented technology

The invention discloses a multi-channel speech enhancement method based on fusion spatial features, which comprises the following steps of: firstly, respectively extracting multi-channel complex spectrum features and fusion spatial features from noisy multi-channel speech; inputting the extracted multi-channel complex spectrum features and fusion spatial features into a deep neural network for processing, setting a processing target as ideal complex value ratio masking, and constructing a deep reconvolution cyclic speech enhancement network based on a reconvolution encoder and decoder structure; and processing the multi-channel complex spectrum features and the fused spatial features by using a deep re-convolution cyclic speech enhancement network, and outputting predicted complex value ratio masking. According to the method, spectral information, direction features, inter-channel features and correlation coefficient features are fully integrated, and spatial modeling is performed on a dynamic sound source, so that the ability of the network to understand and process voice signals is far better than that of a traditional method, complex and changeable voice scenes can be effectively dealt with, and the method is suitable for popularization and application. And the speech enhancement effect in a static scene and a dynamic scene is effectively improved.

The embodiment of the invention provides a millimeter wave radar transmit-receive array error calibration method and related equipment. The method comprises the following steps: acquiring a target virtual channel complex value matrix; determining a target calibration phase matrix according to the target virtual channel complex value matrix; determining a target amplitude-phase calibration factor matrix according to the target virtual channel complex value matrix and the target calibration phase matrix; executing a target phase calibration operation according to the target calibration phase matrix; and executing a target amplitude and phase calibration operation according to the target virtual channel complex value matrix and the target amplitude and phase calibration factor matrix. In this way, the phase error between the transmitting antennas caused by the machining process and the like can be compensated, so that the transmitting gain after synthesis is improved, the probability of distortion of spatial synthesis of a transmitting directional diagram is reduced, the amplitude-phase error between the MIMO virtual arrays is compensated, the accuracy of an angle measurement result is improved, and therefore the spatial synthesis precision is improved; and the radar detection quality and detection precision are improved.

The invention provides an SAR target screening method based on binary super-dimensional calculation, and relates to the technical field of real-time target detection. The method comprises the following steps: directly carrying out real and virtual part solution and sign function quantization on I / Q dual-channel complex data of an original echo of a synthetic aperture radar (SAR) to obtain a binary fusion matrix; constructing an abnormal profile based on a preset background statistical model, and adaptively dividing the quantized data into multiple segments according to the abnormal profile; performing super-dimensional mapping on each segment through an independent random binary projection matrix, generating segmented super-vectors, and splicing the segmented super-vectors into a complete sample super-vector; and finally, target discrimination is realized by calculating the Hamming distance between the target and a pre-stored background / target prototype super vector. The quantization logic can be dynamically switched according to the target size, and the problem that a single quantization strategy is poor in adaptability is effectively solved. And through an adaptive segmentation mechanism of energy perception, the weak target characterization capability is effectively enhanced, and the screening accuracy under a strong clutter background is improved.

The present invention discloses a signal detection system and method for a MIMO system based on parallel Cholesky matrix decomposition, which mainly solves the problems of high complexity in signal detection of existing MIMO systems and doubling of detection delay when performing signal detection in the real number domain. The implementation steps are as follows: the channel matrix conversion module converts the channel complex matrix into a channel real matrix and generates an iterative matrix; the channel matrix decomposition module parallelly decomposes the iterative matrix by using the Cholesky matrix decomposition method; the K-Best parallel search module parallelly searches for paths between adjacent two layers by using the K-Best parallel search method; the detection result decision module completes the detection of signals of all antennas and determines the detection value of the transmitted signal. The parallel design of the signal detection process in the present invention effectively reduces the detection complexity, reduces the detection delay, and can realize signal detection with fewer resource quantities.

The invention provides a radar rainfall measurement method and device based on an oblique 45-degree depolarization ratio, and the method comprises the steps: collecting single-transmitting double-receiving two-channel complex voltage data through employing a radar which achieves the oblique 45-degree polarization through the reconstruction, and calculating an oblique linear depolarization ratio and a reflectivity factor Z; a melting layer is observed and recognized according to a vertical pointing section collected by the radar in a vertical pointing mode, and the upper boundary and the lower boundary of the melting layer are applied to a nearly horizontal scanning mode to achieve liquid rainfall recognition; and carrying out rain attenuation correction on the sum Z, and according to the sum Z after the rain attenuation correction, inverting rainfall parameters such as rainfall intensity, mass weighted average diameter, normalized intercept, number concentration, water content and the like through a lookup table or a neural network. According to the invention, the precision and robustness of rainfall measurement are improved.

Method acquiring a target physiological motion signal, including: acquiring multi-channel complex signals received by multiple channels; and acquiring, from the multi-channel complex signals using a motion signal synthesis vector corresponding to a target motion signal, a target motion complex signal with interference removed; acquiring data received by multiple channels, the data including data without external interference in a first sub-period and data with external interference in a second sub-period; acquiring an external interference suppression matrix based on the data, and acquiring external interference suppression data based on the data without external interference or the data with external interference and the external interference suppression matrix; acquiring a motion signal correlation matrix of the target motion signal in frequency domain based on a frequency range of the target motion signal; and using, as a motion signal synthesis vector, an eigenvector acquired according to an eigenvalue of the motion signal correlation matrix.

The invention discloses a vertical measurement ionogram O / X wave separation method based on polarization matched filtering, and the method comprises the steps: obtaining a dual-channel complex signal, and carrying out the incoherent accumulation to obtain echo intensity; using an OS-CFAR detector and morphological processing to generate a mask; calculating a phase difference based on the mask, and fitting through a double-Gaussian mixture model to obtain O-wave and X-wave characteristic phases; estimating a complex polarization ratio; constructing a polarization matched filter weight vector, multiplying the polarization matched filter weight vector with the dual-channel signal, and separating to obtain an O / X wave complex signal matrix; a separated ionogram is generated through amplitude calculation and detection. The method does not depend on circular polarization hypothesis, actual polarization parameters can be directly estimated, high-precision separation is achieved through polarization matched filtering, and robustness and accuracy are improved.

The invention discloses a multi-channel noise suppression method based on a two-stage deep complex network, and the method comprises the steps: constructing a deep complex gating convolution attention network, and carrying out the correlation modeling of a complex frequency spectrum; enhancing a multi-channel noisy signal by using a two-stage deep complex network; in the beam forming stage, amplitude and phase enhancement is carried out on the multi-channel complex spectrum, and a single-channel complex spectrum with coarse noise reduction is obtained; and in the post-filtering stage, refining processing is carried out on the single-channel complex frequency spectrum subjected to coarse noise reduction, and noise is further suppressed. According to the method, a noise suppression task is divided into a beam forming stage and a post-filtering stage through a two-stage enhancement strategy, the problems of noise residue and target sound signal distortion are effectively avoided, in addition, a complex frequency spectrum is modeled by adopting a complex network structure in the two-stage network, and the noise suppression efficiency is improved. Meanwhile, the amplitude and phase information of the signal are enhanced, the phase distortion is reduced, and the quality and naturalness of the signal are improved.

The invention discloses a phase noise measurement method and system based on vector network analyzer hardware, and relates to the technical field of communication measurement instruments and meters, and the method comprises the steps: receiving dual-channel ADC data, preprocessing the dual-channel ADC data to obtain preprocessed baseband complex IQ data, carrying out the phase analysis of the preprocessed baseband complex IQ data to obtain phase data, and calculating the phase noise of the baseband complex IQ data according to the phase data. Performing cross-correlation operation and amplitude analysis on the phase data to obtain phase noise data; carrying out amplitude analysis on the preprocessed baseband complex IQ data to obtain amplitude data, carrying out dual-channel complex cumulative average operation on the amplitude data to obtain amplitude modulation noise data, and processing to obtain a phase noise spectrum result and an amplitude modulation noise spectrum result; and performing peak detection and stray judgment on the phase noise spectrum result and the amplitude modulation noise spectrum result to obtain a stray identification result, and performing stray elimination on the stray identification result to obtain a final phase noise measurement value and an amplitude modulation noise measurement value.

The present invention discloses a multi-channel noise suppression method based on a two-stage deep complex network, comprising constructing a deep complex gated convolutional attention network to perform correlation modeling on complex spectra; utilizing a two-stage deep complex network to enhance multi-channel noisy signals; in the beamforming stage, performing amplitude and phase enhancement on the multi-channel complex spectrum to obtain a coarsely denoised single-channel complex spectrum; and in the post-filtering stage, performing refinement on the coarsely denoised single-channel complex spectrum to further suppress noise. The method of the present invention divides the noise suppression task into two stages, beamforming and post-filtering, through a two-stage enhancement strategy, effectively avoiding the problems of residual noise and distortion of the target acoustic signal. In addition, by adopting a complex network structure in both stages to model the complex spectrum, the amplitude and phase information of the signal are simultaneously enhanced, phase distortion is reduced, and the quality and naturalness of the signal are improved.

The invention discloses a direction of arrival (DOA) estimation algorithm based on a dual-channel complex convolutional neural network under the condition of a low signal to noise ratio. The method comprises the following steps: firstly, taking a complex covariance matrix of an array receiving signal as input, and respectively inputting the complex covariance matrix into a first channel formed by a dilated convolutional layer and a second channel formed by a standard convolutional layer; the method comprises the following steps: firstly, extracting and fusing the amplitude and phase features of a signal through a unique complex convolution mode of a complex convolutional neural network (CV-CNN), fusing the features extracted by two channels, and outputting an angle classification result through a full connection layer and a sigmoid function. A simulation experiment result shows that a complex convolutional neural network (CV-CNN) has a faster convergence rate than a real convolutional neural network (RV-CNN), and the CV-CNN has higher estimation precision than the RV-CNN under the conditions of a low signal to noise ratio and few snapshots. And compared with the CV-CNN, the asymmetric dual-channel DOA estimation model (CVDCNN) of the complex convolutional neural network has the advantage that the convergence rate and the estimation precision are further improved.

The invention provides an antenna test method and system for a 4D millimeter wave radar, and the method comprises the steps: generating a simulation target with a preset distance and a preset non-zero speed through a radar echo simulator, collecting the point cloud data of a to-be-tested radar at a plurality of rotation angles, and carrying out the screening of the data in two dimensions, namely the distance and the speed, so as to achieve the test of the antenna of the to-be-tested radar. The simulation target point is accurately identified from static environment clutters, complex frequency spectrum data of each channel corresponding to the target point are extracted, and the gain and phase characteristics of the antenna are determined according to the change of the complex frequency spectrum data along with the rotation angle. A traditional microwave darkroom is not needed, environmental clutter interference is effectively restrained through a signal processing means, and therefore the cost and the space requirement of a calibration system are remarkably reduced, portability and test flexibility are improved, and meanwhile the accuracy of a calibration result is guaranteed.

The invention discloses a channel estimation method for an orthogonal time-frequency space modulation system. Aiming at the time-varying channel estimation problem in high dynamic scenes such as satellite communication and the like, the method comprises the following steps: firstly, inserting a single pilot symbol into a time delay-Doppler grid of a system transmission signal, and setting a protection symbol area; at a receiving end, traversing pilot frequency receiving signals according to time delay coordinates, detecting effective paths by setting a threshold value, performing correlation operation by using a Doppler domain basis function, and preliminarily estimating Doppler frequency offset and channel complex gain of each path; and gradually optimizing multi-path parameter estimation by adopting an iterative fitting mode and calculating and updating residual pilot response until a stopping criterion is met. According to the method, the calculation complexity is remarkably reduced while the estimation precision similar to that of a traditional method is kept, the method is particularly suitable for a multi-path sparse channel with fractional Doppler, and an efficient and reliable channel estimation solution is provided for an OTFS communication system with limited resources.

The invention relates to a multi-channel voice signal noise reduction method and device. The method comprises the following steps: acquiring a multi-channel vibration signal and a pure noise signal acquired by a distributed optical fiber acoustic sensing system; performing framing processing on the multi-channel vibration signal to obtain a frequency domain complex spectrum of each frame of each channel; determining a spectral entropy and a low-frequency signal-to-noise ratio according to the pure noise signal, and performing adaptive adjustment on an over-reduction coefficient according to the spectral entropy and the low-frequency signal-to-noise ratio to obtain a target over-reduction coefficient; performing spectral subtraction operation on the first amplitude spectrum according to a target over-reduction coefficient, and performing phase correction on the first phase spectrum based on the time delay of each channel to obtain a single-channel complex spectrum of each frame, thereby effectively suppressing non-stationary noise interference and keeping signal phase consistency through self-adaptive adjustment of the over-reduction coefficient and combination of multi-channel phase correction; and performing phase optimization and superposition reduction on the single-channel complex spectrum to obtain a time domain signal after noise reduction and reconstruction, thereby improving the feature fidelity of the vibration signal through phase optimization and superposition reduction.

The invention provides a multi-channel information fusion hydroelectric generating set vibration state identification method and system, and the method comprises the steps: firstly carrying out the decoupling of a vibration signal in the orthogonal direction of each bearing measurement surface of a hydroelectric generating set through employing a multi-channel complex signal decomposition technology, obtaining a sub-mode signal, and extracting the characteristics of an orbital circle in the orthogonal direction; carrying out normalization processing on the features, and coding the features into a two-dimensional true color image through a three-channel RGB feature matrix mapping algorithm; and inputting the two-dimensional true color image into a deep convolutional neural network with dynamic feature enhancement capability. And mapping the extracted depth features into vectors required by classification through a full connection layer, and outputting a hydroelectric generating set fault diagnosis result. The method can effectively solve the technical problems of insufficient vibration signal feature extraction of a plurality of bearing measurement surfaces and poor data model diagnosis adaptivity in hydroelectric generating set fault diagnosis, and significantly improves the fault recognition precision and robustness under strong noise and complex working conditions.

This disclosure provides a method, apparatus, and pilot tone device for acquiring target physiological motion signals. The method includes: acquiring multi-channel complex signals received from multiple channels; and obtaining at least one target motion complex signal with interference removed from the multi-channel complex signals using a motion signal synthesis vector corresponding to at least one target motion signal. Specifically, acquiring data received from multiple channels within a set time period includes data without external interference within a first set sub-time period and data with external interference within a second set sub-time period; obtaining an external interference suppression matrix based on this data, and obtaining data with suppressed external interference based on the data without external interference or the data with external interference and the external interference suppression matrix; obtaining a motion signal correlation matrix of at least one target motion signal in the frequency domain based on the frequency range of at least one target motion signal; and using an eigenvector obtained from the eigenvalues ​​of the motion signal correlation matrix as a motion signal synthesis vector.

The application discloses a signal processing method, system and related components of a microwave micro-motion sensor, and the signal processing method comprises the following steps: acquiring an initial digital signal corresponding to a transmission signal; performing wavelet decomposition on the initial digital signal to obtain a plurality of decomposed components; removing the decomposed components corresponding to interference signals from all the decomposed components; reconstructing the remaining decomposed components to obtain a reconstructed signal; performing FFT operation on the reconstructed signal to obtain an FFT operation result; using a multi-channel complex band-pass filter to obtain a plurality of complex numbers corresponding to a plurality of preset FFT indexes from the FFT operation result; and determining whether the complex numbers exceed corresponding threshold values, and if so, determining that there is a living body in the current detection range. The application removes interference quantities from the initial digital signal by wavelet decomposition, and the obtained reconstructed signal is subjected to subsequent calculation, so that the detection of whether there is a living body in the current detection range is more accurate and is not affected by the interference signals.

The application provides an AGV communication channel modeling method and system based on an ellipsoid geometry random model, and belongs to the technical field of wireless communication channel modeling; the method comprises the following steps: a three-dimensional digital scene covering multiple frequency bands, AGV speeds and container densities is constructed, and a multi-dimensional channel data set is constructed based on ray tracing; a dynamic ellipsoid geometry model with a fixed base station and an AGV as the focus is constructed, and a time-varying characteristic is introduced by updating the AGV position in real time; the communication channel is divided into three parts, namely, a line-of-sight part, a ground reflection part and a non-line-of-sight part, the channel complex gains of the three parts are respectively modeled, and are superimposed to synthesize the channel impulse response under the FR3 frequency band. Through the introduction of the speed-dependent Doppler effect and the density-dependent metal scattering mechanism, the time-varying non-stationary characteristics of the AGV channel in the metal dense scene of the port can be accurately described, and efficient and accurate theoretical model support is provided for the intelligent port AGV communication design.

This invention discloses a DOA estimation method based on a dual-channel complex convolutional neural network. The method first formulates the DOA estimation problem as a multi-label classification task, using the complex covariance matrix of the array received signal as input. This input is fed into the first channel, composed of standard convolutional layers, and the second channel, composed of dilated convolutional layers and pooling layers. The dilated convolutional layers increase the receptive field of the feature map without losing angular information. The features extracted from the two channels are fused and then output through a fully connected layer and a sigmoid function to achieve angular classification results, thus realizing DOA estimation under multi-source input. This invention further improves the accuracy of DOA estimation. Simulation results show that the dual-channel complex neural network feature fusion method has a better convergence speed, and under conditions of low signal-to-noise ratio and few snapshots, this method has higher estimation accuracy.

This invention discloses a decoupling whitening fast convergence method suitable for active road noise control systems. The steps include: (1) hardware configuration in an ARNC system based on a feedforward strategy; (2) decomposing the secondary path into a minimum-phase part and an all-pass part based on internal and external integral solutions, then filtering the output of the control filter through the minimum-phase part and filtering the error signal through the all-pass part; (3) decomposing the spectral density matrix of the reference signal to obtain the spectral factor function; (4) using the filtered error signal and the whitened reference signal, combined with the minimum mean square algorithm for frequency domain filtering error, updating the control filter, and outputting the control signal to drive the cancelling speaker to emit sound, which propagates through the secondary path and coherently superimposes with the road noise signal near the ear. This invention can be used in ARNC systems in multi-channel complex scenarios, accelerating the convergence speed of the FeLMS algorithm through secondary path decoupling and reference signal whitening operations.

This invention relates to the field of physical layer secure communication technology, and provides a method and apparatus for synchronously generating group keys and paired keys. The method includes: a central node acquiring the azimuth angles and corresponding channel complex gains of each edge node, designing the switching array parameters of a time modulation array based on the azimuth angles of each edge node, calculating the average radiation pattern coefficients of all edge nodes, and transmitting them collectively; the central node generating a group key and modulating it into a modulated signal using the time modulation array and transmitting it; the central node generating corresponding paired keys for each edge node and encrypting them to obtain paired key ciphertext, which is then sent to the corresponding edge node; each edge node receiving the modulated signal performs coherent demodulation to obtain demodulated symbols, performs hard decision on the demodulated symbols, and obtains the group key; each edge node decrypts the paired key ciphertext using the average radiation pattern coefficients and the demodulated symbols to obtain the paired key.

The present invention discloses a communication perception integrated signal processing method based on dual time scales, including: introducing dual time scales in the communication perception integrated system, utilizing the characteristic that the state of scatterers in the channel remains unchanged within the coherence time of multiple channels, combining pilot signals received within the coherence time of multiple channels, and performing channel estimation and environmental perception; performing path-by-path delay compensation and receiving beamforming on the received signal within each coherence time, and demodulating the signal; combining the demodulated communication signals and pilot signals within the coherence time of multiple channels, and performing Doppler frequency estimation on each path; performing delay and Doppler alignment on the remaining received signals, and performing path-by-path beamforming, and demodulating the signal according to the estimated Doppler frequency and delay. The present invention realizes single-carrier low peak-to-average ratio inter-code interference-free communication and high-precision environmental perception, and improves the spectrum efficiency of the communication perception integrated system.

The invention discloses an emergency communication optimization method for a low earth orbit satellite heterogeneous network. The method comprises the following steps: calculating propagation loss of a communication link in the low earth orbit satellite heterogeneous network and gain in a linear array direction, and calculating a received signal of a ground terminal and an intensity ratio of the signal to interference plus noise; the energy efficiency of the low-orbit satellite and the ground network is maximized by optimizing the transmitting power of a ground base station, then the optimization problem of the transmitting power is solved through a continuous convex approximation algorithm, and regularization factors are introduced into the optimization problem of the transmitting power and limiting conditions of the optimization problem. According to the method, the antenna array radiation gains of the satellite base station and the ground base station and the signal transmission path length are calculated respectively, and the energy efficiency of the whole network is maximized by adjusting the transmitting power, the beam forming strategy and the resource allocation based on a channel recoil response function expression and path loss characteristics through an optimization algorithm.

The invention discloses a multi-channel fusion-based vital sign signal feature extraction method. The method comprises the following steps of: modeling a vital sign signal caused by thoracic cavity movement; performing frequency mixing processing on the multi-channel radar human body target echo signal to obtain a multi-channel sampling intermediate frequency signal, and obtaining a one-dimensional range profile of the echo signal; performing moving target display processing to realize static clutter background cancellation, and extracting a multi-channel complex signal of a target distance unit; on the basis of multi-dimensional singular spectrum analysis, a decomposition form of a track matrix is obtained, decomposition sub-matrixes are divided into disjoint groups, and sub-sequences with the same signals are superposed to obtain principal component reconstruction complex signals of all channels; and performing time-frequency analysis and weighted fusion on the reconstructed complex signals of each channel to obtain a fused time-frequency spectrum, tracking and extracting a ridge line of the time-frequency spectrum, obtaining a phase signal containing thoracic cavity motion characteristics, and extracting vital sign parameters. According to the method, the non-contact human body target respiration and heartbeat frequency parameter estimation precision under the low signal-to-noise ratio condition can be improved.

The present invention discloses a processing method for single-channel human physiological signals during the sleep period. First, a high-precision sensor is used to collect single-channel complex signals; a method of constructing a matrix space is used to extract the features of the single-channel complex signals, and through the mapping of different features, a high-dimensional matrix space is obtained, which contains the features of at least two or more signals; then a specific feature basis is used to perform a space transformation on the high-dimensional matrix space to separate the features related to the sleep quality of the test object from the high-dimensional matrix space; and then a gating decision filtering method is used to perform complex modeling on the separated single-channel feature signal group using multi-parameter indicators, and through pattern recognition, gating filtering separation of the single-channel complex signals is achieved. This method can achieve higher-precision filtering and separation of complex signals such as physiological signals during the sleep period, expanding the flexibility and application range of sleep monitoring devices.

The invention discloses a robust active road noise control method based on coherence constraint. The method comprises the following steps: (1) performing hardware configuration on the active road noise control system; (2) calculating a multi-coherence coefficient of the error signal and the reference signal in real time, and using the multi-coherence coefficient as a step length constraint factor for controlling updating of a filter; (3) performing control filter updating through secondary path decomposition and reference signal whitening in combination with a frequency domain filtering error minimum mean square algorithm; and (4) converting the control filter to a time domain, outputting a control signal in real time to drive a cancellation loudspeaker to produce sound, propagating the sound through a secondary path, and coherently superposing the sound with a road noise signal at an error microphone to produce a quiet zone near a human ear. The method can be used in an active road noise control system in a multi-channel complex scene, a higher convergence speed and a lower steady-state error are obtained in a steady-state scene based on a coherent constraint stability protection method, and the robustness of the system is improved in a noisy scene.

The invention provides an antenna rotating speed control method and system and a readable storage medium, and relates to the technical field of radar system control, and the method comprises the steps: receiving a radar echo signal, carrying out the pulse compression processing, and obtaining a difference channel complex signal and a sum channel complex signal; calculating an echo ratio amplitude value based on the difference channel complex signal and the sum channel complex signal, and multiplying the echo ratio amplitude value by a scaling coefficient to obtain a scaling ratio amplitude value; obtaining a current frame antenna rotating speed instruction according to the arc tangent value of the scaling ratio amplitude value and the maximum rotating speed of the antenna; determining antenna steering according to the ratio phase of the difference channel complex signal to the sum channel complex signal; wherein the rotating speed and the rotating direction of the antenna are independently calculated according to each frame of data, and continuous tracking of the target is achieved. The system comprises an echo receiving module, a pulse compression module, an arc tangent control module, a steering judgment module and a driving module. According to the invention, when the radar tracks the target, the rotating speed and the rotating direction of the antenna are controlled, the overshoot and lag probability is effectively reduced, and the target loss probability is further reduced.

Embodiments of the present application provide a millimeter wave radar transceiving array error calibration method and related equipment. The above method comprises: obtaining a target virtual channel complex value matrix; determining a target calibration phase matrix according to the target virtual channel complex value matrix; determining a target amplitude and phase calibration factor matrix according to the target virtual channel complex value matrix and the target calibration phase matrix; performing a target phase calibration operation according to the target calibration phase matrix; and performing a target amplitude and phase calibration operation according to the target virtual channel complex value matrix and the target amplitude and phase calibration factor matrix. In this way, the present application can compensate for the phase error between the transmitting antennas caused by the processing process and the like, improve the synthesized transmitting gain, reduce the probability of distortion of the spatial synthesis of the transmitting directional diagram, compensate for the amplitude and phase error between the MIMO virtual arrays, improve the accuracy of the angle measurement result, thereby improving the spatial synthesis accuracy, improving the radar detection quality and detection accuracy.

The application discloses a kind of robust active road noise control methods based on coherence constraint.The steps include: (1) the hardware configuration of active road noise control system is carried out;(2) the multiple coherence coefficients of error signal and reference signal are calculated in real time, and it is used as the step constraint factor of control filter update;(3) the update of control filter is carried out by secondary path decomposition and reference signal whitening, combined with frequency domain filtering error minimum mean square algorithm;(4) the control filter is converted to time domain, and control signal is output in real time to drive cancellation loudspeaker to sound, and after propagating via secondary path, it is coherent superposition with road noise signal at error microphone, and quiet zone is generated near human ear.The application can be used in active road noise control system in multi-channel complex scene, and the stable protection method based on coherence constraint is obtained faster convergence speed and lower steady-state error in steady-state scene, and the robustness of system is improved in noisy scene.

The invention relates to the technical field of industrial equipment fault diagnosis, and discloses a paper machine fault diagnosis method based on multichannel grouping, which comprises the following steps: firstly, preprocessing and re-sampling signals of different physical types, and grouping according to a vibration group, a pressure and current group and a temperature group; inputting each group of time sequence windows into a multi-scale time-frequency dynamic attention module to extract time-frequency attention features, obtaining gently changed time sequence representation through slow feature layer constraint, reconstructing by an intra-group long and short term memory auto-encoder, and calculating a time sequence point reconstruction error and a window aggregation error; and finally, completing fault judgment by combining the static threshold value and a self-adaptive dynamic threshold value based on normal window sliding statistics. The method realizes high-precision modeling and adaptive anomaly detection of multi-channel complex time sequence signals, has the advantages of high diagnosis precision, low false alarm rate, small detection delay and the like, and is suitable for online monitoring and intelligent operation and maintenance of the paper machine.