14 results about "Signal subspace" patented technology

The application discloses a kind of real-time detection method and system of food pollutant based on intelligent sensor, comprising: through the multimodal sensor array of deployment in food processing production line key station, sensor response signal is collected;Baseline response under the condition of no pollution is predicted by establishing dynamic baseline prediction model;Signal separation network based on variational decoupling autoencoder is constructed, residual signal is mapped to low-dimensional latent space by sharing encoder and is divided into baseline error subspace and pollutant signal subspace, respectively by two independent decoders reconstructing baseline prediction error signal and pollutant candidate signal;Multi-scale time-frequency transform is carried out to pollutant candidate signal to identify pollutant type and estimate concentration;Through distributed intelligent agent cooperation, pollution traceability and hierarchical early warning are carried out;Cross-scene adaptive calibration is realized using meta-learning framework.The application realizes the real-time decoupling of pollutant signal and matrix change signal in food processing process.

This invention discloses a method and system for separating AIS aliasing signals using a four-circular array antenna. The steps are as follows: S100: AIS signals are received through multiple antennas to obtain the spatial baseband signal matrix X, and the phase error Δφ is calculated using interval calculation. k S200: Calculate the phase compensation matrix p; S200: Compensate the baseband signal according to the phase compensation matrix to obtain the baseband signal X. comp S300: For baseband signal X comp By expanding the spatial and temporal dimensions, we obtain the spatiotemporal snapshot matrix X. st (t); S400: The covariance matrix R is obtained by estimating the spatiotemporal snapshot matrix. xx S500: For the covariance matrix R xx Eigenvalue decomposition is performed to obtain the signal subspace and noise subspace; S600: DOA estimation is performed using the MUSIC algorithm to construct the spatial spectrum P(θ); S700: The beam w at the optimal solution is obtained using the space-time constrained minimum variance algorithm; S800: The separated signal is output through the sparse separation method of protocol features. This method can significantly improve the availability of AIS self-organizing network and provide more reliable protection for ship navigation safety.

The application discloses a method for suppressing main lobe interference of a multi-station radar system based on oblique projection. The method aims to solve the problem that the estimation of noise subspace is not accurate due to the energy leakage of interference, so that the noise subspace is no longer orthogonal to the interference subspace, and the orthogonal projection anti-interference performance is invalid. The implementation steps of the application include: directing the receiving antenna beams of each radar station in the multi-station radar system to the target and the area where the jammer is located; aligning the echo baseband signal vectors of each radar station in the multi-station radar system in the time domain to obtain a receiving signal matrix of the radar system; generating a covariance matrix; estimating a signal subspace; constructing an oblique projection operator; and multiplying the receiving signal matrix of the radar system by the oblique projection operator to obtain a signal after interference suppression. The application has the advantage that the suppression type interference signal entering from the main lobe of the radar station antenna can be effectively suppressed in a low signal-to-noise ratio scene.

The application provides a two-dimensional DOA estimation method for arbitrary array single-base MIMO radar based on data rearrangement, and relates to the technical field of array sensor direction finding. The rank structure of a coherent signal covariance matrix is recovered by rearranging a received data matrix through arbitrary array element positions of a transmitting array and a receiving array; S2: eigenvalue decomposition is performed on the signal covariance matrix to obtain a signal subspace; S3: reliable rough estimation is obtained through rotation invariance; S4: an integer ambiguity vector corresponding to spatial phase ambiguity caused by array element spacing greater than half a wavelength is solved; S5: the phase of an array flow pattern with phase ambiguity is compensated, and an accurate estimation value of the 2D-DOA of the target is calculated; the high-precision 2D-DOA estimation problem in a complex scene of a coherent signal source, arbitrary array geometry and phase ambiguity caused by part of array element spacing greater than half a wavelength is solved, unambiguous and high-precision angle parameter estimation is realized, and the estimation accuracy and algorithm reliability are improved.

This invention discloses a sparse Bayesian target azimuth estimation method based on far-field dictionary reconstruction under strong near-field interference. The method includes subspace decomposition of the observation data to construct a signal subspace projection operator; projecting and normalizing the far-field dictionary to obtain a projected reconstruction dictionary, which replaces the far-field dictionary in sparse Bayesian learning, and jointly performing sparse Bayesian iterative estimation with the near-field dictionary; finally, outputting the far-field target azimuth estimation result. This method effectively partitions the subspace and constructs a projection matrix accordingly to suppress the impact of strong near-field interference on far-field azimuth estimation, significantly improving the far-field target azimuth estimation capability of the sparse Bayesian method under strong near-field interference conditions.

The application discloses a power line channel estimation method, device and medium, and the method comprises the following steps: S1, a Vandermonde structure manifold matrix containing multipath delay information is constructed; S2, based on the Vandermonde structure manifold matrix, a super-complete manifold matrix containing all potential delay point positions is constructed, and a channel estimation problem is converted into a sparse representation problem; S3, an unconstrained optimization objective function is constructed; S4, singular value decomposition is performed on received OFDM pilot signal data of multiple time periods, a signal subspace is extracted, and compression and noise suppression of sampling data are realized; S5, the data compressed through step S4 is substituted into the optimization objective function constructed in step S3 to obtain high-precision multipath delay estimation; and S6, based on the multipath delay estimation, a complete power line channel response is reconstructed, and high-resolution and high-robustness channel estimation results are obtained in a power line channel environment with high noise and strong multipath interference at a relatively low calculation complexity.

The application provides a radar signal processing method and device based on joint ESPRIT, acquires non-uniform slow time sampling data of a radar, and constructs a corresponding signal model; calculates a time difference according to the sampling data, and constructs a corresponding equivalent shift relationship in combination with the signal model; extracts a subspace for the sampling data to obtain a corresponding signal subspace; stacks the equivalent shift relationship and the signal subspace to obtain a joint shift relationship; and calculates a target Doppler frequency according to the joint shift relationship. Without changing the existing radar hardware structure, the method realizes coherent fusion of cross-frame data by unified modeling and subspace processing on segmented sampling data, thereby significantly improving the Doppler estimation accuracy and resolution.

The application discloses a single-snapshot direction of arrival (DOA) estimation method and system for quantum honey bear foraging mechanism under impact noise, and relates to the field of array signal processing. In order to solve the problem that the performance of most existing algorithms will significantly decline and the DOA estimation work is difficult to implement under the impact noise environment, the technical points of the application include: constructing a signal model under single-snapshot sampling for a uniform linear array, establishing a low-order matrix based on a hyperbolic tangent kernel median deviation correlation entropy, and further obtaining a single-snapshot weighted signal subspace fitting equation based on the hyperbolic tangent kernel correlation entropy; and constructing a quantum honey bear foraging mechanism to efficiently solve the subspace fitting equation based on the hyperbolic tangent kernel median deviation correlation entropy. The single-snapshot DOA estimation method provided by the application can effectively measure the direction of arrival of independent sources or coherent sources under the impact noise environment.

The application relates to an anti-mainlobe interference adaptive monopulse angle measurement method for a uniform linear array, which comprises the following steps: obtaining array echo data and calculating a covariance matrix; estimating the number of signal sources and the mainlobe interference angle by using the covariance matrix; constructing a mainlobe interference blocking matrix by using the mainlobe interference angle, and preprocessing the array echo data to obtain preprocessed array data; projecting a corresponding static steering vector of a beam direction to a signal subspace to obtain a sum beam projection vector; performing Wiener filtering on the preprocessed array data by using the sum beam projection vector to solve sum beam adaptive weights; calculating a difference beam projection vector by using the sum beam projection vector, and solving difference beam adaptive weights by using the difference beam projection vector; and calculating a target deflection angle by using the sum beam adaptive weights and the difference beam adaptive weights and adopting an amplitude comparison monopulse method. The method has the advantages of high anti-mainlobe interference capability and high angle measurement precision of a target near the mainlobe interference.

This invention discloses a method for identifying the frost state of the target area of ​​a cryoablation needle based on image processing, relating to the field of cryoablation needle technology. The method involves constructing an original image sequence of the target area in a unified spatiotemporal coordinate system; outputting corresponding degradation stage information; obtaining a high-fidelity clear image sequence of the target area; aggregating signal subspace features through a channel gating mechanism to obtain a signal enhancement feature map set; generating a multi-scale semantic feature tensor of the target area; outputting an initial frost probability distribution map of the target area; performing morphological constraint segmentation optimization to obtain a morphologically consistent frost region mask; obtaining a temporally stable frost region mask sequence and constructing a dynamic evolution model of the frost state, displaying warning information in real time. This invention effectively suppresses the feature submersion problem caused by mixed noise and significantly improves the recognizability and stability of the underlying structural information of the target area in a low-temperature imaging environment.

This invention discloses a robust beamforming method based on interference signal steering vector estimation, comprising: determining a projection matrix of the signal subspace based on the sample covariance matrix of the echo signal; determining a projection matrix of the interference subspace based on the covariance matrix of the interference signal; projecting the hypothetical interference signal steering vector multiple times onto the signal subspace and the interference subspace using the projection matrices of the signal subspace and the interference subspace respectively, to obtain a final projected steering vector; estimating the estimated steering vector of the interference signal based on the final projected steering vector; and forming a robust beam based on the estimated steering vector of the interference signal. According to the method provided by this invention, by projecting the hypothetical interference signal steering vector multiple times onto the signal subspace and the interference subspace respectively, the influence of array correction errors such as array position errors and array structure errors, as well as array element amplitude and phase errors, on beamforming performance can be effectively reduced, achieving robust beamforming.

The application discloses a wall-penetrating human activity recognition method based on CSI, which comprises the following steps: collecting 5GHz frequency band signals emitted by a signal source and calculating channel state information (CSI); separating human dynamic components by using a reference channel; segmenting action segments and static segments, and obtaining signal subspaces thereof through eigenvalue decomposition; selecting two antennas to perform conjugate multiplication, and obtaining a time-frequency spectrum through Butterworth band-pass filtering, PCA and short-time Fourier transform; and inputting the signal subspaces and the time-frequency spectrum into an attention-enhanced neural network to recognize human activities. The application has the advantages of high signal collection efficiency, high analysis accuracy and strong reliability.

This invention relates to the field of array signal processing, and particularly to a DOA estimation method for non-Gaussian colored noise interference scenarios. The method includes: acquiring signals under non-Gaussian spatial colored noise interference via an antenna receiving array; calculating the covariance matrix based on the received array data, and constructing a training set, a validation set, and labels based on the covariance matrix; constructing a DOA estimation model, which includes a self-attention-enhanced covariance matrix reconstruction module and a channel attention-enhanced angle estimation module connected in sequence; constructing a training loss function for the DOA estimation model, the loss function of the covariance matrix reconstruction module including signal subspace loss components, diagonal element loss components, off-diagonal element line loss components, and phase loss components; and training the DOA estimation model using a supervised offline training method based on the training set, validation set, and training loss function. This invention exhibits stronger robustness and higher DOA estimation accuracy in mixed environments with low signal-to-noise ratio non-Gaussian colored noise.