51results 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 invention discloses a system Gaussian filter design method for multi-step measurement of random delay and colored measurement of noise. A first-order autoregression model is introduced to whiten the colored measurement noise, and a system with the colored measurement noise is converted into a nonlinear system with normal white noise; modeling delay measurement data by means of a Bernoulli random variable, processing multi-step random delay by taking new system measurement noise as a state increment, realizing one-step prediction of delay measurement information, and finally establishing a nonlinear system state model and a measurement model with multi-step measurement random delay and colored measurement noise. And an unscented transformation mode is adopted to approximate non-Gaussian weighted integral, and an unscented Kalman filtering algorithm under the conditions of multi-step measurement random delay and colored measurement noise is provided. According to the method, the accuracy of the filter in processing multi-step measurement random delay and colored measurement noise is improved, the deviation in a filtering result is reduced, and higher precision and better numerical stability are achieved.

The invention belongs to the technical field of retired battery sorting, and particularly relates to a retired battery secondary sorting method, system and equipment based on random charging fragments and a medium. The method comprises the steps of generating a single battery identification code, constructing a battery code database, and performing charging and discharging control on a single battery based on a preset charging and discharging strategy. And obtaining a time sequence sample data set of temperature, voltage and capacity change, converting the time sequence sample data set into two-dimensional image data and storing the two-dimensional image data, and training a prediction model through a two-dimensional convolutional neural network to estimate the maximum available capacity of the battery so as to realize secondary sorting of the battery. The system comprises a code scanning module, a charging and discharging control module, a data acquisition module, a data processing module and a sorting module. The method can reduce test time and energy consumption, improve battery sorting efficiency and precision, reduce memory occupation and rear-end model operation time, and is suitable for efficient and accurate sorting of retired batteries.

The invention relates to an adaptive sparse channel estimation method applied to a reconfigurable intelligent surface (RIS)-assisted millimeter wave large-scale MIMO system. According to the method, two progressive algorithms, namely an NLMS-LOG algorithm and an NLMS-NLMF-LOG algorithm, are designed, and the method aims at making full use of the sparsity of millimeter wave channels so as to improve the accuracy and convergence speed of channel estimation. According to the NLMS-LOG algorithm, a logarithm and a penalty term are innovatively introduced into a cost function, so that the convergence speed is effectively increased; the NLMS-NLMF-LOG algorithm fuses the fast convergence advantage of the NLMS algorithm and the excellent performance of the NLMF algorithm under the condition of low signal-to-noise ratio, and the precision of channel estimation is further improved. Simulation experiment results prove that the scheme provided by the invention is obviously superior to the prior art in the aspects of channel estimation precision and convergence speed.

The invention relates to a scene-proportion-unknown super-large-scale MIMO mixed field channel estimation method based on an OMP algorithm. The method comprises the following steps: constructing a signal model from a sending end to a receiving end; calculating a Rayleigh distance according to the transmitting end antenna array and the receiving end antenna array; constructing a far-field channel model and a near-field channel model between the transmitting end and the receiving end according to the calculated Rayleigh distance; constructing a mixed field channel model between the transmitting end and the receiving end according to the constructed far-field channel model and near-field channel model; and solving the mixed field channel model by using an OMP algorithm with an unknown scene proportion, and estimating the mixed field channel between the sending end and the receiving end. According to the method, the signal can be effectively estimated in a scene in which the proportion of far-field and near-field path numbers is unknown.

The invention relates to a method for estimating high-resolution atmosphere carbon dioxide concentration in a land area of China, which comprises the following steps of: (1) calculating a vegetation index, a surface water index and a vegetation phenology index by using satellite data, and generating a vegetation data file; (2) generating a carbon dioxide background field file and a meteorological background field file by using a CAMS global greenhouse gas concentration data set and a fifth-generation atmosphere reanalysis data set of the European mid-term weather forecast center; (3) generating a human-derived carbon dioxide emission data file by using a multi-scale emission list of the Xinghua University; (4) constructing an optimized atmospheric carbon dioxide simulation system; and (5) inputting the vegetation data file, the carbon dioxide background field and meteorological background field file and the man-made source carbon dioxide emission data file into the optimized atmospheric carbon dioxide simulation system, and operating to obtain a high-resolution atmospheric carbon dioxide concentration estimation result in the land area of China. The method can significantly improve the ability to estimate the carbon dioxide concentration of the atmosphere in the land area of China.

The invention provides a five-phase FTFSCW-IPM novel approach rate FTSMC control method based on time-varying ESO, and the method comprises the following steps: building a five-phase FTFSCW-IPM motor mathematical model which comprises a flux linkage, a voltage, a torque and a motion equation; designing a fast terminal sliding mode surface and an improved reaching law to obtain a control law of a five-phase FTFSCW-IPM improved reaching rate fast terminal sliding mode in a rotating speed loop, and performing controller stability analysis in combination with a Lyapunov theorem; a time-varying parameter ESO is designed to be used for five-phase FTFSCW-IPM disturbance observation estimation, the characteristics and parameters of an observer are analyzed, an improved index reaching rate control method is introduced, a feedforward compensation strategy based on a time-varying extended state observer is combined, and the robustness and anti-interference performance of a five-phase FTFSCW-IPM motor control system are improved.

The present invention provides a channel estimation method for a MIMO communication system assisted by an intelligent reflecting surface, applies deep learning technology to the channel estimation of the intelligent reflecting surface, and introduces a conditional generative adversarial network with a received signal as conditional information, thereby effectively improving the accuracy of channel estimation and improving robustness performance by reducing information loss during neural network training; adopts a U‑Net structure and a patching structure in the generator and the discriminator respectively to improve the ability of the generator to generate more realistic data and the ability to identify input detailed information; proposes an optimization loss function for updating the parameters of the generator to maintain the correct optimization direction during the training process, thereby further improving the channel estimation performance of the conditional generative adversarial network in the adversarial training process of the generator and the discriminator. The channel estimation method for a MIMO communication system assisted by an intelligent reflecting surface based on the conditional generative adversarial network of the present invention effectively improves the channel estimation performance of the MIMO communication system assisted by the intelligent reflecting surface.

The invention relates to the technical field of aircraft system fault detection, in particular to an aircraft system fault detection method based on an unknown input observer. According to the method, joint estimation of the state variable and the fault signal of the nonlinear aircraft system can be realized, and the influence of external disturbance on the estimation performance is effectively reduced. According to the method, the defects of an existing fault detection method based on residual signals can be overcome, the fault occurrence time can be provided, the estimated value of the fault amplitude can also be provided, the fault can be further processed in the later period, and the method has wider and profound theoretical significance and practical application value; estimated values of state variables and fault signals of the system can be provided at the same time, faults can be detected in time, and a certain foundation can be laid for monitoring dynamic behaviors of the system in real time and completing specific control tasks.

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 invention discloses a sky wave large-scale MIMO-OFDM (Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing) channel smooth beam structure precoding transmission method and system. A sky wave large-scale MIMO-OFDM base station designs a beam structure precoder of each user in combination with a group of subcarriers on the basis of constraining the equivalent channel smoothness of each user receiving end by using a beam-based channel model, and pre-codes signals sent to each user by using the channel smooth beam structure precoder to realize downlink transmission. The method comprises the following steps of: firstly, pre-coding a data signal and a pilot signal which are sent to each user by a base station on each subcarrier by using a beam domain precoder, and mapping a beam domain pre-coded signal of each user to a complete beam domain sending signal based on a space beam set of each user; and finally, converting the sum of the beam domain sending signals of the users into a spatial domain sending signal. According to the method, the equivalent channel estimation performance can be effectively improved and the signal detection performance can be remarkably improved while the sky wave large-scale MIMO-OFDM downlink precoding transmission performance is ensured.

The invention relates to the technical field of signal processing, in particular to a multi-path channel pseudo code sequence estimation method and system based on RAKE receiving and MLSE estimation, and the method comprises the steps: carrying out the signal processing of a received signal under a multi-path channel, obtaining pseudo code period estimation, and carrying out the rough estimation of the time delay and amplitude of each path of the received signal; constructing a multi-path channel receiving signal model based on the time delay and amplitude coarse estimation of each path, so that the multi-path channel receiving signal model performs signal symbol detection and pseudo code estimation by using a self-interference cancellation (SIC) and RAKE receiving detection algorithm; an information symbol and pseudo code sequence joint estimation cost function is set based on a multi-channel receiving signal model, and iterative optimization estimation is carried out on the multi-channel receiving signal model by using maximum likelihood estimation (MLSE) based on the cost function. And taking the pseudo code sequence and the information symbol sequence corresponding to the iteration result with the minimum cost function value during model convergence as final estimation results and outputting the final estimation results. The method can be suitable for signal processing requirements under the conditions of low signal-to-noise ratio and various parameters.

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 present invention discloses a distributed safety state estimation method, device and storage medium for an information-physical system, belonging to the technical field of information-physical systems. The distributed safety state estimation method for an information-physical system includes: performing observable decomposition on a system dynamic model to obtain a local observable matrix pair; obtaining a gain matrix of a distributed observer according to the local observable matrix pair and the local unobservable matrix; communicating based on a local system state vector to obtain a local system state vector after communication; obtaining an output system state vector of a distributed observer based on the gain matrix and the local system state vector after communication; and controlling an adaptive attack compensation function in the distributed observer to compensate for a deception attack signal, so as to achieve consistency between the output system state vector and the system state vector in the system dynamic model. The present invention can solve the problem of state estimation of information-physical systems deployed on a large scale under the interference of deception attacks.

The present invention designs a remote estimation sensor scheduling decision method based on a threshold structure; considers two modes of sensor transmission of raw data and event trigger mechanism, and limits the two transmission modes to have the same communication rate; under this framework, uses the Kalman filter algorithm to model the information transmission mode of the sensor and the estimation process of the remote state estimator; then, considering the time delay D caused by the data preprocessing method, proposes an estimation performance evaluation index based on the mean square error, and calculates the threshold of the time delay D by constructing a balance model based on the mean square error of the two transmission modes; finally, designs a remote estimation sensor scheduling decision method based on a threshold structure, realizes the reasonable scheduling of the sensor for the above transmission mechanism under different conditions; effectively estimates the real state of the system, and improves the estimation performance of the estimator under the condition of limited transmission energy, which has certain application value in engineering practice.

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 present invention belongs to the field of maneuvering target detection, and particularly relates to a maneuvering target coherent detection method and system based on distance frequency axis inversion transform and second-order WVD, by pulse compressing radar echo signals, frequency inverting the pulse compressed signals along the distance frequency dimension, and gathering target echoes into the same distance unit; using second-order WVD to perform time-frequency transform on the echo signals in the distance unit, and estimating the target acceleration through Fourier transform processing; using a phase compensation function to construct the compensated distance frequency-slow time domain dimension signal that is the same as the uniform motion model, performing variable delay autocorrelation along the slow time dimension, and then performing Fourier transform on the slow time variable after decoupling through addition operation and scale Fourier transform to achieve coherent accumulation, and then estimating the actual speed and initial distance of the maneuvering target. The present invention can ensure parameter estimation performance and effect while reducing computational complexity, and is convenient for application in actual scenarios.

The invention relates to the technical field of wireless communication, in particular to a near-field broadband non-stationary channel estimation method for super-large-scale MIMO (Multiple Input Multiple Output), which comprises the following steps of: based on a near-field broadband non-stationary channel model, analyzing a linear relationship between position parameters and frequency by utilizing matching of a received signal and a polar domain sample, and acquiring a position parameter most relevant to each physical path; according to the linear relation between the position parameter and the frequency, the deviation relation of the frequency-related visible area is analyzed; based on the deviation relation of the visible area, rough estimation and accurate estimation are respectively designed by using antenna receiving signal power, the frequency-related visible area range is analyzed, and the accurate estimation value of the channel gain is confirmed; and accumulating the channel reconstruction result according to the position parameter, the visual area and the channel gain in each physical path under the same frequency so as to recover the whole broadband channel, and optimizing the channel estimation process by using the offset relation of the visual area to realize efficient reconstruction.

The present invention provides a MIMO scenario channel estimation method based on fully connected deep learning. The method comprises the steps of: constructing a data set; constructing a deep learning model based on fully connected deep learning; repeatedly training the deep learning model, and recovering an equivalent channel matrix through prediction data, comparing the matrix with its corresponding channel state information label, and evaluating the output of the deep learning model through minimum mean square error; obtaining an optimized deep learning model for obtaining channel state information through verification data. The present invention focuses on learning OTFS pilot information through a fully connected network to complete the channel estimation task, significantly improving the accuracy of channel estimation and reducing the overhead of pilot resources while maintaining high performance.

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 present application discloses a battery parameter estimation method, device and storage medium. The present application relates to the field of electrical digital data processing technology. The battery parameter estimation method includes: determining an iteration parameter according to the Jacobian matrix corresponding to the initial value of the battery parameter, wherein the initial value of the battery parameter is the historical battery parameter at the previous calculation moment; determining the battery parameter at the current calculation moment based on the iteration parameter and the historical battery parameter, wherein the battery parameter includes at least one of a concentration field variable, an electric potential field variable and a body current variable. The present application solves the technical problem in the related art that the battery state estimation takes a long time under the condition of strong nonlinearity of the battery parameters, thereby achieving the technical effect of reducing the computational overhead and improving the efficiency of battery state estimation.

The invention discloses a multi-dimensional feature aggregation network channel estimation method based on an AUV wireless communication system, and the method comprises the following steps: collecting channel frequency response and receiving signal data of an AUV in an electromagnetic environment when the AUV works on a water surface, and making a data set; constructing a multi-dimensional feature aggregation network model based on a self-attention mechanism; performing offline training on the multi-dimensional feature aggregation network by using the data set; performing data preprocessing on the received signal of the AUV to obtain input data; and loading the network model after offline training, and inputting the input data into the network for channel estimation. According to the multi-dimensional feature aggregation network channel estimation method based on the AUV wireless communication system, a self-attention mechanism in deep learning is used for building a multi-dimensional feature aggregation network model, the estimation performance is far higher than that of a traditional channel estimation method, the space-time complexity of the network is very low, and the method is easy to implement. The method can be applied to an AUV wireless communication system in an offline training mode.

The present invention relates to the field of robot visual perception technology, specifically a three-dimensional scene reconstruction method based on cross-channel attention mechanism feature fusion, including: S1, collecting multiple binocular stereo vision images to be three-dimensionally reconstructed after distortion correction and stereo correction, and constructing a training set; S2, constructing a three-dimensional scene reconstruction network; S3, selecting a binocular stereo vision image to input into the three-dimensional scene reconstruction network, and obtaining the predicted disparity of each different stage; S4, constructing a loss function, calculating the loss between the predicted disparity and the real disparity of each different stage, and looping from S3 to S4 until the loss function converges; then updating the weight of the three-dimensional scene reconstruction network; S5, testing the trained three-dimensional scene reconstruction network, and obtaining the three-dimensional depth information of the binocular stereo vision three-dimensional reconstruction scene. The present invention greatly improves the efficiency of context feature fusion and improves the binocular stereo vision disparity estimation performance.

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 invention belongs to the technical field of signal processing. The invention provides a complex scene DOA estimation method based on matrix reconstruction. The method comprises the following steps: acquiring a received signal, and obtaining a signal covariance matrix of the received signal according to the received signal; performing eigenvalue decomposition on the signal covariance matrix to obtain a signal eigenvector corresponding to the signal subspace; extracting a maximum signal feature vector corresponding to a maximum feature value of the signal feature vectors, and performing element rearrangement on the maximum signal feature vector to obtain a reconstruction matrix; and performing DOA estimation based on the reconstruction matrix in combination with a Root-MUSIC algorithm based on polynomial reconstruction so as to obtain a DOA estimation value. According to the embodiment of the invention, the correction matrix is a square matrix, and DOA estimation in a complex scene can be realized only through eigenvalue decomposition. Moreover, the method is high in success probability, is lower in signal-to-noise ratio threshold, and is good in estimation performance.