17results about How to "Improve estimation performance" patented technology

The invention discloses a pilot optimization method for large-scale MIMO channel estimation based on structural compressed sensing. The method comprises the steps of establishing a channel estimation model for a large-scale MIMO-OFDM (Multiple-Input-Multiple-Output-Orthogonal Frequency Division Multiplexing) system when pilots are placed in an overlapping mode; simplifying the channel estimation model for the large-scale MIMO-OFDM system, thereby enabling the channel estimation model to correspond to a structural compressed sensing model; and obtaining an optimum pilot matrix through utilization of a pilot optimization algorithm. Through adoption of the optimum pilot matrix, according to the channel estimation of the large-scale MIMO system based on structural compressed sensing, the mean square errors MSEs of the channel estimation are clearly reduced, and the channel estimation performance is improved.

The application discloses a self-disturbance rejection control method and system based on an error compensation type extended state observer, and the method comprises the following steps: a linear self-disturbance rejection controller acquires input signals and each output state of an extended state observer, and outputs a first control signal; the linear self-disturbance rejection controller acquires input signals and each output state of the extended state observer, and outputs the first control signal; an estimation error model of the extended state observer for output displacement is constructed, a controlled object is converted into an integrator series type form, and an output displacement signal is obtained based on the controlled object; the output displacement signal and a control input signal are input into the extended state observer, and each output state is obtained; and then the linear self-disturbance rejection controller and the output displacement model are fed back. The estimation capability of the observer is greatly improved without increasing the bandwidth of the observer; the error compensation type disturbance observer is combined with the self-disturbance rejection control, and the tracking performance of the self-disturbance rejection control is further improved.

The application discloses a kind of robust direction of arrival estimation method based on ADMM-Net, by expanding ADMM algorithm into model-driven deep network ADMM-Net to improve DOA estimation accuracy, speed up DOA estimation speed and have robustness to array disturbance.Firstly, the sparse transformation of source and array received multi-shot data is carried out by space over-complete dictionary, and the DOA estimation is converted into compressed sensing sparse recovery problem;Then, ADMM algorithm is expanded, and model-driven deep network ADMM-Net with explainability is formed;ADMM-Net is used to reconstruct source power spectrum and carry out DOA estimation.The present application can learn the hyperparameters in iterative algorithm and array disturbance, solve the problem that existing compressed sensing DOA estimation method based on calculation speed is slow, and fails under the condition of array disturbance, realize fast, accurate, explainable and robust DOA estimation.

The application discloses a deep learning DOA estimation method based on original IQ data. The application uses I and Q components of the original signal as the input of the model to improve the performance. The application aims to solve the DOA estimation problem of a single signal source, and models the single signal source DOA estimation problem as a single-label multi-classification problem. By discretizing the DOA range, the possible directions of arrival are taken as corresponding labels. A convolutional neural network is designed to adapt to different numbers of snapshots, and accurate DOA estimation can be adaptively obtained for input signals of different lengths. Experimental results show that, compared with existing deep learning DOA estimation methods based on covariance matrix as input, the scheme has more excellent performance, and can provide a more reliable solution for array signal processing.

The invention discloses a signal transmission method and device, a terminal, network side equipment and a medium, and belongs to the technical field of communication, and the signal transmission method comprises the steps that the terminal sends a first signal based on a first parameter; wherein the first signal corresponds to the first channel, and the first parameter is related to at least one of the following items: the type of the first channel, the resource allocation position of the first channel, the sending power of the first channel, the modulation order of the first channel, the waveform of the first channel, the PAPR of the first channel, the type of the first signal, the number of ports of the first signal, and the number of symbols of the first signal; a sequence of the first signal, a PAPR of the first signal.

This invention belongs to the field of underwater robot control technology, specifically providing a dual-field-of-view cooperative perception and adaptive weighted localization method and system for underwater robots in confined spaces. The method utilizes a pre-calibrated heterogeneous dual-field-of-view camera system on the robot to acquire main and auxiliary field-of-view images. Image enhancement is performed through red channel compensation and contrast-limited adaptive histogram equalization, and image quality indices are calculated. An analytical pose estimation algorithm based on microplanes is employed to detect visual markers and calculate their poses, obtaining two sets of camera pose observations. The two sets of observations are then weighted and fused using calculated fusion weights to obtain the robot's center visual pose. An error-state Kalman filter is constructed, using the visual pose as the observation input, and dynamically adjusting the observation noise covariance matrix based on the fusion weights to output the final pose. This invention can achieve highly robust and high-precision localization in complex underwater environments.

The invention discloses a multi-extended target joint tracking and classification method based on star convex RHM and LMB filters, and belongs to the field of radar target tracking. According to the method, an LMB parameter set is initialized by using target prior information, and then a sensor measurement set is divided through a mean shift algorithm; then, state prediction is achieved by combining survival target parameter updating and new target parameter sampling, and then measurement updating is completed through LMB-to-GLMB, GLMB updating and GLMB-to-LMB; and then trimming and fusing the LMB parameter set, estimating the number of targets, extracting state information, and circularly executing until observation is finished. According to the method, a star convex RHM modeling expansion state is adopted to reduce dimensions, the low detection probability / high clutter scene performance is improved based on an LMB framework, the tracking classification effect and the real-time performance are both considered, and the engineering application value is high.

The invention discloses a multi-sensor management and control method based on multi-agent deep reinforcement learning, and the method comprises the steps: firstly building a state equation and a motion model of a target, and an observation model and a motion model of a sensor node; then constructing a multi-agent strategy learning module; the multi-agent strategy learning module adopts an MAPPO algorithm; then, each intelligent agent processes measurement information in the vision field range of the intelligent agent to obtain a multi-dimensional matrix state diagram and a search utility diagram; and splicing the two information with the state information of the agents, inputting the spliced information to a multi-agent strategy learning module, outputting and executing the control action of each agent at the next moment, and repeating the steps to realize the global estimation of the target in the monitoring area. According to the method, while the bottleneck of centralized calculation is effectively avoided, the estimation capability of target information outside a visual field is expanded, and deep fusion of GM-PHD probability information and a reinforcement learning reward mechanism is realized, so that the global estimation and tracking performance of a system on multiple targets is remarkably 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 invention relates to a channel estimation method and device, communication equipment, a chip and a chip module. The method comprises the following steps: performing singular value decomposition on a time domain autocorrelation matrix and a frequency domain autocorrelation matrix of a target channel to obtain a time domain unitary matrix and a time domain eigenvalue corresponding to the time domain autocorrelation matrix and a frequency domain unitary matrix and a frequency domain eigenvalue corresponding to the frequency domain autocorrelation matrix; updating the time domain feature value based on the frequency domain feature value to obtain an updated time domain feature value; updating the frequency domain feature value based on the updated time domain feature value to obtain an updated frequency domain feature value; obtaining a time domain filtering coefficient based on the updated time domain characteristic value, and obtaining a frequency domain filtering coefficient based on the updated frequency domain characteristic value; and obtaining a filter coefficient based on the time domain filter coefficient and the frequency domain filter coefficient, and performing channel estimation based on the filter coefficient to obtain a channel estimation result of the target channel. By adopting the method, the channel estimation performance loss can be reduced.

The application proposes a large-scale MIMO-OFDM channel acquisition method based on multiple groups of adjustable phase shift pilots. In the application, users are divided into multiple groups, each group uses the same basic pilot sequence to generate multiple adjustable phase shift pilots, and different groups use different basic pilot matrices; the autocorrelation matrix of the basic pilot matrix is a unit matrix, and the sequence after FFT transformation of the diagonal element of the cross-correlation matrix of different basic pilot matrices is sparse, wherein each non-zero complex element has the same argument. In the channel acquisition method, each user terminal sends known and scheduled multiple groups of adjustable phase shift pilot signals to the base station, the base station pre-processes the received signals, and then completes channel estimation. The method can greatly improve the spectral efficiency and channel information acquisition accuracy of the large-scale MIMO-OFDM system, especially in the communication scene with a large number of users and strong mobility, and has superior performance.

The application discloses a remote fault diagnosis method for an aircraft system based on a hybrid observer and belongs to the technical field of fault diagnosis.The application fully considers the sparse available measurement values caused by a transmission network, makes up for the shortage of an existing estimation method designed based on continuous measurement values, fully utilizes a new generation of information technology, and effectively improves the reliability of an information physical system operation of an aircraft; the information physical system framework is introduced into the field of remote fault diagnosis of the aircraft system, a remote fault diagnosis scheme is established through system modeling, simulation verification and hybrid system theory, and the development of the health management technology of the aircraft system is promoted to a certain extent.

The invention provides a depth estimation model training method and device, and the method comprises the steps: carrying out the conversion from a panorama to perspective of a to-be-processed panorama, and obtaining a perspective image corresponding to the panorama; inputting the perspective view into a perspective view depth estimation model obtained through supervised training in advance to obtain a reference perspective depth map corresponding to the perspective view; inputting the panorama into a panorama depth estimation model obtained through self-supervised training in advance to obtain a panorama depth map; and performing iterative training on the panorama depth estimation model according to the reference perspective depth map and the panorama depth map. According to the method, empirical knowledge of the perspective view depth estimation model is migrated to the panorama depth estimation model in a model distillation mode, and iterative training of the panorama depth estimation model is realized under the condition of considering relatively low training cost and relatively high estimation accuracy.

This application discloses a three-dimensional spatial PM2.5 concentration estimation method, device, medium, and product, relating to the field of atmospheric environmental monitoring. The method includes: stitching together haze images from multiple perspectives within a sample area to obtain a three-dimensional spatial sample image; the three-dimensional spatial sample image and the corresponding actual PM2.5 concentration value constitute a training sample; inputting multiple training samples into an improved VIT model for training to obtain a PM2.5 concentration prediction model; the improved VIT model includes a multi-scale image feature embedding module and a Transformer encoder module arranged sequentially; inputting the three-dimensional spatial sample image corresponding to the area to be detected into the PM2.5 concentration prediction model to obtain the corresponding estimated PM2.5 concentration value. This application can obtain accurate estimated PM2.5 concentration values.

The application discloses a kind of forging step beam servo control system and method, solve the problem of poor performance of prior art step beam control system, with better security, faster response speed Advantage, specific scheme is as follows: a kind of forging step beam servo control system, including industrial computer, controller, data acquisition module, control module, industrial computer is connected with controller, controller is separately connected with data acquisition module, motor control module, motor control module includes driver and motor, driver is connected with motor, motor is connected with ball screw mechanism to distinguish step beam or gripper;Industrial computer is installed with the motion control software for parameter setting and motor motion control program writing;Controller receives instruction from industrial computer and real-time information transmitted by data acquisition module and handles, judges the state and position of motor, compares with motor setting track, state, finally transmits real-time monitoring information to industrial computer.

The invention provides a feature extraction and fusion reconstruction method, an electronic device, a medium and a product, the feature extraction and fusion reconstruction method comprises the following steps: obtaining channel time sequence data, and carrying out dimension standardization and time sequence arrangement on the channel time sequence data to obtain preprocessed data; pCA dimension reduction processing is carried out on the preprocessed data, and static features are extracted; sampling the preprocessed data by adopting a sliding window, and constructing a time sequence sample; extracting dynamic characteristics of the time sequence samples; performing full-connection mapping on the static features; performing feature splicing and regression output on the static features and the dynamic features; and training, testing, evaluating and visualizing the output result. According to the feature extraction and fusion reconstruction method, the static space feature and the dynamic time-varying feature of the channel can be precisely restored, high-precision channel modeling and reconstruction of a millimeter wave communication system in a dynamic environment are achieved, robustness, stability and channel estimation performance are good, delay is short, and generalization ability is high.

The invention discloses an unmanned aerial vehicle anti-interference and noise-reduction control method based on a composite extended state observer, and belongs to the technical field of unmanned aerial vehicle control. A filter is embedded in the extended state observer, so that the influence of zero-mean measurement noise on the attitude angular velocity is reduced, and time-varying interference is extended as a new state quantity, so that the attitude angular velocity of the unmanned aerial vehicle is improved. Effective estimation of time-varying interference is realized, estimation of the time-varying interference is improved, and the influence of measurement noise on the attitude angular velocity, especially control input, is greatly weakened. Time-varying interference is estimated through an extended state observer, and unknown dynamics caused by an external wind field and dynamics influenced by measurement noise are approximated by adopting a radial basis function neural network, so that the estimation performance of lumped interference is effectively improved; in addition, a decoupling term is embedded in the extended state observer, so that the conservative property of the control method is reduced, and the application range of the control method is expanded.