102 results about "Autocorrelation matrix" patented technology

The invention provides an Internet of Vehicles broadcast frame radio frequency fingerprint identification method based on LMMSE channel estimation. The method comprises the following steps: obtaining subcarrier data in a resource grid based on an obtained signal of a physical side link broadcast channel; performing root-mean-square delay expansion and channel autocorrelation matrix construction based on the subcarrier data, and combining priori signal-to-noise ratio regularization and time domain windowing operation to obtain a channel estimation value; initial radio frequency fingerprint features are obtained through the channel equalization and the channel estimation value, and the improved neural network and the initial radio frequency fingerprint features are utilized to carry out classification identification on the Internet of Vehicles equipment. Influences of noise and channels on fingerprints are effectively considered, and the purpose of extracting fingerprints of different devices in a complex environment more accurately is achieved.

The invention provides a video anomaly detection method and system based on time interaction entropy, and the method comprises the steps: obtaining an original video stream, segmenting the original video stream into video clips, generating a time disturbance sample through the video clips, carrying out the feature extraction through a feature extraction module, and solving a mean value, so as to obtain a video feature sequence, processing the video feature sequence by using a time structure entropy calculation module to obtain a time structure entropy sequence, and processing the semantic feature by using an attention modulation module to obtain a structure entropy modulation attention output feature; and processing the global time volatility priori descriptors and the structure entropy modulation attention output features by using an exception scoring module to obtain exception scores. According to the method, lightweight design is adopted, a parameter-free time structure entropy calculation module is designed, and through immediate feature centralization and time autocorrelation matrix calculation in a sliding window, trainable parameters are not needed, so that the calculation overhead is remarkably reduced.

The invention discloses a fault identification method and device for a rotating mechanism, computer equipment and the rotating mechanism. The method comprises the following steps: acquiring rotating speed information and a time domain vibration signal of the rotating mechanism; converting the time domain vibration signal into an angular domain vibration signal based on the rotating speed information; performing feature extraction on the angular domain vibration signal based on the autocorrelation matrix of the angular domain vibration signal and the filter coefficient to update the filter coefficient and obtain a target filter coefficient; and determining fault characteristics of the rotating mechanism based on a target vibration signal obtained by processing the angular domain vibration signal based on the target filtering coefficient. According to the method provided by the invention, the fault features of the unsteady vibration signals of the rotating mechanism can be effectively extracted, so that reliable fault diagnosis of the rotating mechanism is realized.

The invention discloses a power distribution network power prediction method, system and device based on multi-source heterogeneous data fusion, and a storage medium, and relates to the technical field of power distribution network data processing and prediction, and the method comprises the steps: constructing a data autocorrelation matrix based on the comprehensive operation and maintenance data of a power distribution network; performing eigenvalue decomposition on the data autocorrelation matrix, and constructing a principal component matrix; performing dimension reduction processing on zero-mean data obtained in the process of constructing the data autocorrelation matrix by using the principal component matrix to obtain operation and maintenance feature fusion associated data of the power distribution network; an echo state network model is established and trained, the operation and maintenance feature fusion associated data of the power distribution network is used as the input of the echo state network model, and the output power of the power distribution network is predicted; according to the method, the data dimension is reduced, the calculation complexity is reduced, and the output power of the power distribution network can be predicted more accurately; the operation efficiency and the management level of the power distribution network can be improved, and powerful support is provided for stable supply of a power system.

The invention discloses a multi-view heterogeneous cascade non-stationary time sequence prediction method based on Mama improvement, and belongs to the technical field of time sequence analysis. The prediction method comprises the following steps: collecting and preprocessing time sequence data of a target domain; the time sequence data are stabilized and decomposed; the decomposed seasonal part is embedded from a univariate view angle and a multivariate view angle respectively; an embedding result is correspondingly input into a Mama encoder and a multi-granularity cascade Mama encoder decoder heterogeneous module for feature learning; performing stationarity correction on the features based on an autocorrelation matrix; and predicting the feature representation after stability correction and the decomposed trend part, adding prediction results, and carrying out inverse normalization to obtain a final prediction result. The method provided by the invention solves the technical problem that the trend and periodicity of dynamic evolution in data are difficult to capture when an existing method faces a non-stationary time sequence, and also solves the problems that an existing model is high in complexity and low in prediction accuracy.

The present invention discloses a GNSS deception interference detection method and device based on signal direction vector autocorrelation, which relates to the field of satellite navigation technology. The method includes: storing and updating the direction vectors corresponding to each satellite signal and arranging them into a matrix; multiplying the matrix by the conjugate transpose of the matrix to obtain an autocorrelation matrix; calculating the modulus and phase values ​​of all elements of the autocorrelation matrix, and calculating the 1-norm or infinity-norm of the autocorrelation matrix based on the modulus; and judging whether the current satellite signal contains a deception signal based on the 1-norm, infinity-norm or phase value of the autocorrelation matrix. The method provided by the present invention not only enhances and clarifies the role of collaborative detection between multiple satellite signals and improves the accuracy of detection, but also the detection results can be quantified, and the threshold value is easy to set.

The present disclosure provides a data processing method, apparatus and device, a vehicle, a medium, and a program product. The method comprises: acquiring millimeter-wave radar point voxel data corresponding to millimeter-wave radar data; encoding the millimeter-wave radar point voxel data to generate a target voxel sequence feature; performing multi-layer linear computation on the target voxel sequence feature to generate a query sequence feature, a key sequence feature, and a value sequence feature; computing the query sequence feature and the key sequence feature on the basis of an attention mechanism to obtain an autocorrelation matrix; and encoding the autocorrelation matrix and the value sequence feature, and performing residual connection on an encoding result and the target voxel sequence feature to generate a millimeter-wave radar autocorrelation-enhanced sequence feature.

The invention relates to the technical field of radar signal processing, in particular to a deep learning-based target detection method in a sea clutter environment, which comprises the following steps of: acquiring echo signals in the sea clutter environment, arranging the echo signals according to a high-speed time domain and a low-speed time domain, and sampling to obtain a complex data matrix; analyzing the complex data matrix to construct an autocorrelation matrix, and obtaining input data through the autocorrelation matrix; the method comprises the following steps: constructing a self-supervised learning model based on a Transform encoder by adopting input data, and carrying out training; and obtaining data to be detected, inputting the data to be detected into the trained self-supervised learning model based on the Transform encoder to output average similarity, and obtaining a detection result of the sea clutter environment through the average similarity. Conversion analysis is carried out based on the thunder wave signal, the expression ability is improved through the model, the target and the sea clutter are distinguished, the detection ability of the weak target under the strong clutter background is effectively improved, and the problem that the target is submerged by the sea clutter and is difficult to detect under the condition of a low signal-to-noise ratio is solved.

This invention provides a sound source localization method, apparatus, and product based on incremental learning and imbalance correction. The method includes: acquiring a dataset for a new task, a parameter-frozen feature extractor, and regularization parameters; enhancing the tail category of the new task dataset; initializing an autocorrelation matrix, a cross-correlation matrix, and a category count based on the number of categories in the new task dataset; iterating through each sample in the new task dataset, updating the autocorrelation matrix, cross-correlation matrix, and category count; calculating the category weight for each category and calculating the Gini coefficient describing the category distribution; adaptively adjusting the regularization parameters based on the Gini coefficient; and solving for and outputting the optimal weight matrix based on the adjusted regularization parameters and aggregating the global autocorrelation matrix and global cross-correlation matrix of different categories. The optimal weight matrix is ​​used to update the sound source localization model. This invention addresses the problem of intra-task and inter-task imbalance in sound source localization, improving the sound source localization effect.

The invention relates to the technical field of satellite communication, in particular to a low-orbit satellite beam self-adaptive acquisition method based on fused double architectures, which comprises the following steps: realizing local clock calibration by locking a high-orbit satellite broadcast channel and acquiring a target low-orbit satellite almanac; a terminal pose and an array surface temperature gradient are acquired in real time by using an inertial measurement unit and a temperature sensor network, and a multi-dimensional physical characteristic tensor is constructed. Then, inputting the feature tensor into a nonlinear compensation model integrating multi-scale cavity convolution and a spatial self-attention mechanism, and reasoning and outputting a phase pre-distortion matrix; and by executing channel-level Hadamard product operation of the pre-distortion matrix and the theoretical pointing matrix, generating a wave control driving matrix and synthesizing a physical receiving wave beam. After a downlink signal is captured, a real Doppler frequency shift residual error is extracted, an error autocorrelation matrix is constructed, and an almanac compensation weight is reversely corrected. According to the invention, through multi-dimensional feature perception and algorithm logic closed loop, robust beam capture is realized.

The invention discloses an image semantic segmentation method and system based on cross-modal hierarchical knowledge distillation, and the method comprises the steps: inputting an image to a multi-modal teacher model and a multi-modal student model, obtaining teacher features and student features of each stage, and outputting a prediction map; the teacher features of all the stages are fused, common modal features are enhanced, fusion features corresponding to all the stages are obtained, and the fusion features and the student features are divided into high-level features and low-level features according to different stages; the low-level teacher fusion features and the low-level student features are converted into frequency domain graphs respectively, and high-frequency parts are taken out to transmit detail knowledge; respectively calculating a cross-correlation matrix and an autocorrelation matrix by using the high-level teacher fusion features and the high-level student features, and transmitting structural knowledge; and aligning the teacher output prediction map and the student output prediction map, and transmitting response knowledge. According to the invention, the multi-modal features are fused in a mode of modal enhancement, the modal gap between the teacher fusion features and the student features is reduced, and the single-modal student model segmentation precision is improved.

The present application relates to the field of radio signal identification, and particularly relates to a cross-domain few-shot specific emitter identification method based on self-supervised learning and transfer learning, comprising: adopting a self-supervised joint embedding method, pre-training a first model and a second model by using an upstream task pre-training dataset, regularizing a first autocorrelation matrix output by the first model by using von Neumann entropy, calculating the cosine similarity between the feature projections output by the first model and the second model, and updating the parameters of the first model based on the regularized autocorrelation matrix and the cosine similarity; migrating the pre-trained first encoder to a feature extractor, inputting a downstream task fine-tuning dataset into the connected feature extractor and classification head to fine-tune the feature extractor and the classification head; inputting a wireless signal into the fine-tuned feature extractor and classification head to obtain the predicted transmitter category of the signal. The present application achieves good identification accuracy under the conditions of few samples and low cost.

The invention discloses a sparse array DOA estimation method based on a residual network, and the method comprises the following steps: reconstructing an ideal Here-Toeplitz covariance matrix of a virtual uniform linear array through deep learning, and achieving the end-to-end training in combination with the differentiability of a Root-MUSIC algorithm. By introducing the multi-delay autocorrelation matrix tensor and residual learning, the method can effectively process challenging scenes such as coherent signals, a small number of snapshots and broadband signals. The effectiveness of the method is verified in the embodiment, high-precision estimation can be achieved under various conditions, the target that the number of estimated information sources is larger than the number of physical array elements is successfully achieved, and a new solution is provided for sparse array DOA estimation.

The MIMO equalizer circuit (300) includes: a controller (339); a calculation circuit (302) generates and outputs an autocorrelation matrix W (308) in response to receiving the covariance matrix RI (123) and the channel estimation matrix H (121). A matrix calculation circuit (316) that receives and combines the inverse autocorrelation matrix (315) and the channel estimation matrix H (121), and outputs an equalization gain P matrix (324); an inverse autocorrelation matrix (315), a received signal vector y (301), and a channel estimation matrix H (121) are received and combined, and a vector'f '(323) is output. A successive interference cancellation (SIC) detection circuit (325) converts the received P matrix (324) into a first orthogonal unitary matrix Q (328) and a first upper triangular matrix R (327), and converts the matrix Q (328), the matrix R (327), and the f vector (323) into an SIC estimated equalization symbol Z2 vector (330), which is output.

This invention discloses a time series forecasting method, apparatus, program product, storage medium, and its applications, relating to the field of time series analysis, to address the problems of high computational complexity and poor reliability of prediction results in time series forecasting tasks. This invention trains an inference model using time series data. This inference model extracts dependencies between variables based on the autocorrelation matrix of the input original time series; integrates these dependencies into the original time series to obtain key time-series features; learns long-term dependencies between variables from these key time-series features; and maps the time series for future time steps from these long-term dependencies. This invention achieves accurate time series forecasting with high reliability at a computationally low level.

The invention discloses a stratum lithology identification method and system based on a geophysical integrated logging multi-parameter curve, and belongs to the technical field of geophysical logging. According to the method, the original logging data is obtained, after abnormal point processing, smooth filtering and normalization preprocessing are carried out, the autocorrelation matrix is used for solving the weighting factor to synthesize the multi-parameter curve, and then the formation lithology is identified by applying the synthesized curve. According to the method, the multiplicity and limitation of a single logging curve are overcome, accidental errors are reduced, the interpretation precision and credibility of stratum lithology identification are improved, and the method can be popularized and applied to multiple fields such as geological layering and oil and gas prediction and has wide practical value.

The invention relates to a deception jamming detection method based on spatial domain processing gain dynamic change. The method comprises the following steps: receiving and processing a satellite navigation signal by using an array antenna, and estimating an airspace autocorrelation matrix of the satellite navigation signal; then, through actively constructing virtual interference which periodically changes in a direction, calculating and obtaining an array weighting vector which dynamically changes along with time by combining the matrix; then, performing spatial filtering on the signals by using the vector, independently tracking each navigation signal, and estimating a carrier-to-noise ratio time sequence of each navigation signal; if the correlation coefficient exceeds a set threshold, it is judged that the two signals are from the same direction due to the same airspace gain modulation, and the signals are classified as deception signals; and finally, rejecting the identified deception signal from the positioning solution, updating a navigation result and triggering an alarm, thereby realizing robust deception detection in a strong interference environment.

The application discloses a channel estimation method and related device, wherein the method comprises: performing minimum mean square error (MMSE) channel estimation based on the product of the first amplitude factor and the first noise power of the first dimension, the first channel autocorrelation matrix of the first dimension, and the first diagonal matrix to obtain the channel estimation value matrix of the first dimension; wherein the channel estimation value matrix of the first dimension is obtained by performing MMSE channel estimation in the non-last step of the multi-dimensional step-by-step MMSE channel estimation; the first amplitude factor is a positive number less than or equal to 1; and performing MMSE channel estimation based on the channel estimation value matrix of the first dimension, the second channel autocorrelation matrix of the last dimension, the second noise power and the second diagonal matrix to obtain the multi-dimensional channel estimation value matrix. The method can improve the performance of the multi-dimensional step-by-step MMSE channel estimation.

The application relates to a through-wall radar signal enhancement method and device for human motion, wherein the method comprises the following steps: acquiring a to-be-enhanced through-wall spectrogram; inputting the to-be-enhanced through-wall spectrogram into a generator in a network model based on spectrogram processing to obtain an enhanced through-wall spectrogram, so as to monitor human motion. The training method of the network model comprises the following steps: training a pre-training model through free space spectrograms and corresponding free space flip spectrograms in a data set; inputting a through-wall spectrogram into the generator to obtain a corresponding enhanced through-wall spectrogram; inputting the enhanced through-wall spectrogram and a non-paired free space spectrogram into the pre-training model which has been trained to obtain corresponding channel dimension autocorrelation matrices; inputting the enhanced through-wall spectrogram and the non-paired free space spectrogram into a discriminator to obtain a discrimination result; and constructing a loss function to optimize the generator and the discriminator, so that the generator can perform signal enhancement and improve the accuracy of downstream semantic recognition.

An image reconstruction method, comprising: reading raw data; reducing dimensionality of the raw data to obtain a one-dimensional vector; taking auto-correlation of the one-dimensional vector to obtain an auto-correlation matrix; performing eigen-decomposition of the auto-correlation matrix to obtain eigenvalues and eigenvectors of the auto-correlation matrix; sorting the eigenvectors according to their corresponding eigenvalues; taking auto-correlation of a subspace constituted by eigenvectors corresponding to the first N smaller eigenvalues to obtain a target subspace; defining grid positions of a spectral search in a phase encoding direction; for each phase encoding direction, performing the following spectral search steps: defining an array manifold corresponding to the phase encoding direction; obtaining a correlation coefficient of the array manifold corresponding to the phase encoding direction and the target subspace or obtaining a correlation coefficient of an inverse matrix of the array manifold and the target subspace, and then performing a negative correlation transformation on the correlation coefficient to obtain a spectrum; extracting elements from the spectrum to obtain a row of a reconstructed image; and outputting the reconstructed image.

The invention discloses a continuous learning method of a neuromorphic classification network and a neuromorphic classification method, and belongs to the field of image classification. In order to solve the problems that a model in continuous learning CL of a neuromorphic data set easily forgets old knowledge when learning a new task, stability and plasticity are difficult to consider at the same time, and generalization ability is poor in a multi-scene and cross-architecture condition, the invention provides a parameter importance estimation and constraint method based on a gradient statistical structure. By calculating an autocorrelation matrix of gradient information and taking each network layer in the neural morphological classification network as a target, consistency and task correlation of parameter updating directions of each structural unit in each network layer are described, so that fine-grained structural importance representation is obtained. When a new task is learned, the continuous learning method utilizes the autocorrelation matrix to carry out regularization constraint on parameter updating, so that excessive adjustment on a sensitive direction of a historical task is inhibited, and disastrous forgetting is effectively avoided.

An apparatus for encoding a speech signal that determines a codebook vector of a speech coding algorithm includes a matrix determiner for determining an autocorrelation matrix R, and a codebook vector determiner for determining the codebook vector depending on the autocorrelation matrix R. The matrix determiner determines the autocorrelation matrix R by determining vector coefficients of a vector r, wherein the autocorrelation matrix R includes a plurality of rows and a plurality of columns, wherein the vector r indicates one of the columns or one of the rows of the autocorrelation matrix R, wherein R(i, j)=r(|i−j|), wherein R(i, j) indicates the coefficients of the autocorrelation matrix R, wherein i is a first index indicating one of a plurality of rows of the autocorrelation matrix R, and wherein j is a second index indicating one of the plurality of columns of the autocorrelation matrix R.

This application provides a data processing method and a communication apparatus, and relates to the field of communication technologies. The method can be used for downlink data processing and uplink data processing. The communication apparatus may receive a reference signal, and obtain a first matrix based on the reference signal. The first matrix is an autocorrelation matrix whose dimension is N rows and N columns, and N indicates a quantity of channels of an antenna array. Based on the first matrix, channels in different rows of the antenna array are averaged to obtain a second matrix, where a dimension of the second matrix is N / (x×R) rows and N / (x×R) columns; R represents a quantity of channel rows of the antenna array; x represents polarization of the antenna array.

The invention provides a channel estimation method for a minimum mean square error, which comprises the following steps of: performing Fourier transform on a received baseband signal to obtain OFDM (Orthogonal Frequency Division Multiplexing) frequency domain data, and obtaining frequency domain data of all symbols on a receiving side through solution resource mapping; the method comprises the following steps: processing by utilizing frequency domain data of a pilot symbol at a receiving side and a priori known pilot symbol sequence to obtain channel responses of SNR (Signal to Noise Ratio) estimation and LS (Least Square) channel estimation; processing according to the channel response of the LS channel estimation to obtain root-mean-square delay expansion; calculating a data-pilot frequency cross-correlation matrix and a pilot frequency self-correlation matrix according to root mean square time delay expansion, and calculating to obtain an MMSE filtering channel estimation coefficient according to the data-pilot frequency cross-correlation matrix and the pilot frequency self-correlation matrix; mMSE filtering is carried out according to the MMSE filtering channel estimation coefficient, and finally a final channel estimation value is obtained. According to the method, channel response is accurately estimated while operation is simplified, so that reliable communication and low operation delay are ensured.

The invention discloses an AFDM-ISAC system-based communication and inductance integration method, and belongs to the technical field of target distance and speed estimation. According to the invention, the problem of low target distance and speed estimation precision of the current AFDM waveform-based sensing integrated scene is solved. The method comprises the following steps of: performing Fourier transform on a received signal at a radar end of the AFDM-ISAC system, performing matched filtering on a Fourier transform result of the received signal and a transmitting end signal subjected to Fourier transform, performing echo phase information extraction according to the signal subjected to matched filtering, calculating an autocorrelation matrix of the echo phase information, and calculating an autocorrelation matrix of the echo phase information according to the autocorrelation matrix. And carrying out eigenvalue decomposition on the autocorrelation matrix to obtain a noise subspace, constructing a spatial spectrum function by using the property that the noise subspace and a signal subspace in a loop iteration MUSIC algorithm are orthogonal, and searching a spatial spectrum peak in a loop iteration mode by the loop iteration MUSIC algorithm to obtain estimated values of the target distance and the speed. The method provided by the invention can be applied to the high-precision estimation of the target distance and speed of the AFDM-ISAC system.

The invention relates to a channel equalization method and device of a physical uplink control channel format 1 and a medium. In at least one embodiment of the invention, after at least one pilot symbol and a plurality of data symbols are received, an average interference signal covariance matrix corresponding to each resource element on each pilot symbol and a channel estimation dispreading result of the plurality of data symbols are determined; a channel autocorrelation matrix can be calculated by using an average interference signal covariance matrix and a channel estimation and dispreading result, so that a channel equalization result is calculated by using frequency domain data on each pilot symbol, the channel estimation and dispreading results of a plurality of data symbols and the channel autocorrelation matrix. Because the influence of the adjacent region interference is considered, the channel autocorrelation matrix is calculated by taking the average interference signal covariance matrix as a parameter, so that the channel equalization result calculated by using the channel autocorrelation matrix can reduce the influence of the adjacent region interference and other noise sources, the channel equalization performance is improved, and the decoding accuracy is improved.

This application discloses a fault identification method, apparatus, computer equipment, and rotating mechanism for a rotating mechanism. The method includes: acquiring the rotational speed information and time-domain vibration signal of the rotating mechanism; converting the time-domain vibration signal into an angular-domain vibration signal based on the rotational speed information; extracting features from the angular-domain vibration signal based on the autocorrelation matrix and filtering coefficients to update the filtering coefficients and obtain target filtering coefficients; and determining the fault characteristics of the rotating mechanism based on the target vibration signal obtained by processing the angular-domain vibration signal using the target filtering coefficients. The method provided in this application can effectively extract fault characteristics from the unsteady-state vibration signal of a rotating mechanism, thereby achieving reliable fault diagnosis of the rotating mechanism.

The invention provides a low-altitude flexible HMIMO-oriented spatial characteristic evaluation and port configuration method and system, and the method comprises the steps: constructing the array geometry of a flexible HMIMO array, and setting a user position and an electromagnetic propagation environment model; secondly, visibility judgment and gating processing are carried out on the sight distance model; then, the whole array is divided into sub-arrays, and available sub-arrays are screened out; calculating an autocorrelation matrix and quantizing a channel statistical difference distance between any two sub-arrays to generate a spatial non-stationary heat map; meanwhile, the full-aperture space freedom degree is calculated; and finally, obtaining an average achievable port mode number by taking a statistical expectation from a target direction domain, and obtaining a port configuration engineering upper bound by integrating a theoretical degree of freedom and hardware constraints. According to the method, the problems of spatial non-stationary quantization, sub-array availability judgment and port-level realizable mode number determination of the flexible curved surface array under near-field and shielding conditions are solved, and a direct basis is provided for engineering design and resource allocation of flexible HMIMO in low-altitude communication.

This invention discloses a high-resolution corner detection method and system for images based on the second-order Gaussian directional derivative, belonging to the field of image detection. The method includes the following steps: acquiring raw image data and performing second-order Gaussian directional derivative filtering at different directional angles using a preset response separability scale to obtain the directional derivative response value of each pixel in each direction; constructing a directional response vector for each pixel based on the directional derivative response value, and generating an autocorrelation matrix based on the directional response vector; calculating the corner response metric value of each pixel based on the eigenvalues ​​of the autocorrelation matrix; performing local nonmaximum suppression processing based on the corner response metric value, and filtering pixels that meet a preset response threshold to obtain a set of corner coordinates. This invention can solve the problem in the prior art where corner response functions are difficult to effectively separate in structurally similar edge regions.

In some implementations, a device may obtain information associated with one or more targets, wherein the information associated with the one or more targets includes sensing information and a communication performance control parameter. The device may determine a sensing beampattern based at least in part on the information associated with the one or more targets. The device may determine a target sensing autocorrelation matrix for the sensing beampattern. The device may identify a candidate sensing autocorrelation matrix for a joint communication and sensing transmission based at least in part on the target sensing autocorrelation matrix. The device may determine a target sensing precoder based at least in part on the candidate sensing autocorrelation matrix. The device may generate a joint communication and sensing precoder based at least in part on the target sensing precoder and the communication performance control parameter.