139 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 provides a shape function KL expansion random field discretization method considering a slope soil body irregular domain, and belongs to the technical field of soil body parameter random field analysis. Comprising the following steps: establishing a slope numerical model and dividing a discrete grid and a form function grid; establishing a probability distribution model of a soil parameter random field; calculating an element stiffness submatrix ke and an element mass matrix Ee; calculating an autocorrelation matrix rho and a global stiffness matrix B; assembling a global matrix; calculating a characteristic value lambda k, a characteristic function phi k and an expansion term number M; and random field discretization of the slope numerical model is realized. The random field discrete technology provided by the invention is used for efficiently and accurately analyzing the random distribution characteristics of the soil parameters in the rock-soil structure. The discrete random field model can be efficiently constructed in a complex geological structure, the calculation cost is remarkably reduced, the method can be conveniently combined with numerical simulation tools such as a finite element method, and the requirement of actual engineering for efficiency is met while the precision is improved.

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.

A rolling bearing fault feature extraction method based on variable scale multipoint kurtosis deconvolution comprises the following steps: a, sampling a rolling bearing vibration acceleration signal to obtain a rolling bearing fault vibration signal; b, constructing a toeplitz autocorrelation matrix; c, constructing a variable-scale multipoint kurtosis deconvolution filter; d, filtering the rolling bearing fault vibration signal by using an optimal deconvolution filter to obtain a fault impact signal; e, performing envelope demodulation processing on the fault impact signal, extracting an envelope of the fault impact signal, and obtaining an envelope spectrum through spectral analysis; and f, judging the fault type of the rolling bearing according to the envelope spectrum. According to the method, the optimal target vector of the deconvolution is searched by constructing the variable-scale multipoint kurtosis index, the optimal filter is constructed, the fault impact signal is extracted through the deconvolution, the fault impact signal is subjected to envelope analysis, the fault feature frequency of the rolling bearing is extracted, and the fault feature of the rolling bearing can be accurately extracted.

The invention discloses a fault diagnosis method and system based on strong-adaptability image information enhancement, and the method comprises the following steps: segmenting a one-dimensional signal into data segments with equal length, obtaining a feature matrix of each data segment, a row autocorrelation matrix and a column autocorrelation matrix, coding the feature matrixes into images, generating a plurality of images through a plurality of data segments, and storing the images in a database; forming an image information data set after image enhancement; and dividing the image information data set into a training set and a test set, inputting the training set into the double-pooling multi-scale convolutional neural network model for training, then inputting the test set into the trained double-pooling multi-scale convolutional neural network, and outputting a fault diagnosis result. By adopting the technical scheme, effective diagnosis of the mechanical equipment is realized based on image information enhancement and feature fusion.

The invention discloses a mining area load spectrum anomaly detection method based on standard variable analysis, and belongs to the technical field of hydraulic pump anomaly detection.The mining area load spectrum anomaly detection method comprises the steps that S1, standardized data are obtained through data preprocessing, and a typical working condition data set of a hydraulic pump is constructed in combination with working conditions of an excavator; s2, constructing a historical vector and a future vector, and constructing a historical observation matrix and a future observation matrix according to the historical vector and the future vector; s3, constructing a Hankel matrix according to the autocorrelation matrix and the cross-correlation matrix, decomposing the Hankel matrix and determining a model order; s4, mapping the original data to a standard variable space and a residual space, and respectively evaluating the total variable quantity of the standard variable in the state space and the sum of squares of change errors in the residual space; and S5, determining an evaluation threshold value, and if the evaluation threshold value exceeds the control line, judging that the hydraulic pump operates abnormally. The method is based on standard variable analysis, adopts the pressure pulsation data of the hydraulic pump to perform anomaly detection, is sensitive to the internal running state of the pump, is not easily influenced by the external environment, and can perform early warning on faults of the hydraulic pump.

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 invention discloses a short-term power load prediction method and system based on improved DSSFA-SAC-ConvLSTM, and the method comprises the steps: firstly collecting and preprocessing regional historical power load data, then employing DSSFA to analyze and extract features, constructing a feature matrix, generating a distance adjacency matrix and a dynamic adjacency matrix based on a geographic distance and DSSFA features, fusing the distance adjacency matrix and the dynamic adjacency matrix into an autocorrelation matrix, and carrying out the prediction of a short-term power load based on the autocorrelation matrix. Then splicing the feature matrix and the autocorrelation matrix, inputting the spliced feature matrix and the autocorrelation matrix into a ConvLSTM model for training, adjusting a learning rate by adopting Warmup and improved OneCycleLR, and finally mapping an output feature into a power load prediction value; according to the method, key features are extracted through DSSFA, the data dimension is effectively reduced, important information is reserved, a dynamic adjacency matrix is generated in combination with a geographic distance and a self-attention mechanism, spatial information is fully considered, the accuracy of multi-region prediction is improved, the ConvLSTM learning rate is optimized by adopting a Warmup and an improved OneCycleLR strategy, the model training effect is enhanced, and the prediction efficiency is improved. And the accuracy of power load prediction is further improved, so that the power load prediction method has more excellent performance in a complex scene.

Systems and methods for quantum linear prediction include autocorrelations formed with QFTs, and a modified quantum HHL circuit that includes appropriate normalization and encoding steps for solving a linear system of equations, including normalization of the quantum autocorrelation sequence using a norm factor; measuring a probabilistic distribution associated with values of a quantum state solution vector representing a set of quantum autoregressive parameters that correlate with a linear relationship between the quantum autocorrelation matrix and the quantum autocorrelation sequence; and generating a set of quantum linear prediction coefficients by re-normalization of the quantum state solution vector using the norm factor associated with the quantum autocorrelation sequence of the preprocessed input.

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 invention provides a channel estimation method and device, electronic equipment, a storage medium and a chip, and relates to the technical field of communication. Comprising the following steps: determining a least square LS estimation result corresponding to each pilot frequency point according to a received reference signal and a pilot frequency sequence; according to the LS estimation result, determining a time domain autocorrelation matrix of every two adjacent orthogonal frequency division multiplexing (OFDM) symbols and a frequency domain autocorrelation matrix of N pilot frequency points in the two adjacent OFDM symbols; determining a first eigenvalue matrix of the time domain autocorrelation matrix and a second eigenvalue matrix and eigenvectors of the frequency domain autocorrelation matrix; based on the first eigenvalue matrix, the second eigenvalue matrix and the eigenvector, determining a minimum mean square error (MMSE) filtering matrix corresponding to each OFDM symbol in the two adjacent OFDM symbols; and determining a channel estimation result of each frequency point on each OFDM symbol according to the MMSE filtering matrixes corresponding to every two adjacent OFDM symbols and the LS estimation result. Therefore, the difficulty and complexity of channel estimation are reduced, and the efficiency of channel estimation is improved.

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.

The present invention provides a method and apparatus for realizing modulation signal equalization. The method includes: obtaining a received signal, and determining a first training sequence according to the received signal; performing power compensation on the received signal and the first training sequence to obtain an adjusted signal and a second training sequence; determining an autocorrelation matrix according to the second training sequence; performing matrix correlation operation on the second training sequence based on a known training sequence to determine a cross-correlation matrix; performing iterative solution based on the steepest descent method according to the autocorrelation matrix and the cross-correlation matrix to obtain equalization filter parameters; and performing signal equalization on the received signal according to the equalization filter parameters and the adjusted signal. The present invention uses the steepest descent method to iteratively solve the equalization filter parameters, obtains equalization filter parameters with similar accuracy, significantly reduces the complex resource occupation in the implementation process of the least mean square error method, and realizes high-efficiency equalization of the received signal.

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.