465results about How to "Reduce estimation error" patented technology

A wireless communication apparatus compensates for phase and amplitude imbalances existing among transmit and receive branches, while also preventing likelihood information and SNR estimation error produced by such imbalance compensation. In an apparatus having a plurality of transmit and receive antennas and respective branches for each antenna, a calibration processor multiplies receive signals in each receive branch by antenna calibration coefficients, in order to correct phase and amplitude imbalances existing among the receive branches. A transmit beamforming matrix estimator then estimates a transmit beamforming matrix by using the multiplied receive signals. An estimator then solves for estimated values such as the noise power, likelihood information, and channel waveform equalization values for each receive branch, and in addition, derives a final estimated value that takes the multiplication by correction coefficients into account when averaging the estimated values for each receive branch or when computing a weighted average according to likelihood.

The invention provides a power battery electric charge quantity estimation method. The method includes the following steps of firstly, obtaining the function relationship between the SOC and an open-circuit voltage through an open-circuit voltage method; secondly, measuring the initial value of the SOC; thirdly, sampling and obtaining the SOC estimated initial value of an unscented Kalman filter; fourthly, conducting Sigma point sampling of UT conversion of the unscented Kalman filter according to a battery state equation and an observation equation, obtaining an estimated value of the observation quantity, and estimating an SOC estimated value and a covariance of a power battery at the next moment. According to the method, the open-circuit voltage method and the unscented Kalman filter are used in cooperation for conducting SOC estimation, the estimation accuracy is high, the SOC initial value is obtained through the open-circuit voltage method, SOC estimation modification is conducted, the estimation accuracy is improved, and in the unscented Kalman filter non-linearization approximation process, errors are reduced, the calculation speed is high, and the SOC estimation efficiency is improved.

The invention discloses a method for estimating the charge state of a power cell, which comprises the following steps that: 1. a continuous state space model capable of expressing the relationship of each element in an expression circuit is obtained by an equivalent circuit model of a power cell; the relationship between the open-circuit voltage and the cell charge state is obtained through the standing experiment of the power cell, and the cell charge state is introduced into the continuous state space model of the power cell as a state variable; the power battery model under the noise environment is obtained by combining the noise information; and finally the continuous state space model is subjected to linearization and discretization so as to obtain a linear discrete state space model; 2. a relationship curve of the open-circuit voltage and the cell charge state is obtained through the standing experiment of the power cell, and a parameter k and a parameter d in the power cell model are obtained approximately; 3. parameters of the power cell model are obtained through the identification of current and voltage data collected by a data collecting system; and 4. the power cell charge state is estimated by utilizing a moving horizon estimation method based on the determined power cell model.

The invention discloses an estimating method for the initial value of the stage of charge of a high-accuracy lithium ion battery and belongs to the technical field of automobile power batteries. The estimating method comprises the following steps: firstly, according to a corresponding relation curve of the open-circuit voltage and an SOC (Stage of Charge) at different temperatures, estimating an open-circuit voltage stage of charge (SOCocv) through the lookup table; secondly, according to standing time of the battery, the current voltage platform of the battery and the battery pack temperature, obtaining a power-on initial stage of charge through the three-dimensional lookup table; finally, according to the self-discharging characteristic of the battery, correcting the initial stage of charge. Through the adoption of the method disclosed by the invention, an accumulated error caused by inaccuracy of the initial value estimated through an ampere-hour integral method and the estimation error of stage of charge caused by static current loss in the shutdown process are effectively reduced on the basis of factors of the characteristics of the open-circuit voltage at different temperatures, the standing time of the battery, the self-discharging characteristics of the battery and the like, and the higher accuracy is achieved. The method is suitable for SOC initial value estimation of a single battery, a module and a battery assembly.

The invention relates to a spacecraft fault tolerance attitude cooperation tracking control method based on a normalized neural network, and belongs to the technical field of spacecraft formation flight. According to the method, an attitude motion model of a single spacecraft is built, errors are defined, a control law is designed for the model, a sliding mode function is designed, the derivative of the sliding mode function is solved, an error model is obtained, a control law based on an input normalized neural network is designed, and the states of the spacecrafts are cooperative and consistent; each spacecraft calculates the required control torque according to attitude information of itself and the adjacent spacecraft, the calculated control torque is acted on the corresponding spacecraft via an execution mechanism of each spacecraft, the angular velocity is solved via an obtained attitude dynamic equation, a unit quaternion attitude tracks an expected attitude via the attitude dynamic equation, and consistent attitude of the spacecraft formation is finally realized. According to the method, estimation errors of non-linear function approximation are reduced, the calculating time is reduced, and the convergence rate and the control precision of the system are increased.

The invention relates to a lithium battery charge state estimating method and belongs to the technical field of batteries of electric vehicles. The lithium battery charge state estimating method aims at estimating the charge state of a lithium battery under the complex working conditions of charging and discharging at different multiplying power levels through an estimation method based on a parameter time varying observer. The lithium battery charge state estimating method specifically comprises the step that a battery charge state is regarded as a state variable to be introduced into a lithium ion battery continuous model, the upper limit of hysteresis voltages is determined according to the charging and discharging open-circuit voltage, the battery hysteresis phenomenon is considered to be a first order dynamic process related to the current absolute value, a battery polarization voltage model with parameters changing along with currents and an internal resistance model with parameters changing along with currents are structured through RC rings, battery model end voltages are structured, and a nonlinear parameter time-varying battery model is obtained. The lithium battery charge state estimating method is based on a parameter time-varying lithium ion battery equivalent circuit model, the model parameters are calibrated to be a function of current multiplying power, the characteristics of the battery can be accurately expressed, and meanwhile an existing estimation method can be easily used.

The invention discloses an SOC estimation method based on a Kalman filtering method, and belongs to the field of power battery management. The method specifically comprises the steps that 1, a power battery model is built, and a precise lithium power battery model is built mainly by determining an equivalent circuit model and based on test data; 2, power battery SOC is estimated; 3, EKF algorithmis adopted to estimate an SOC result and conduct error analysis. The method provided by the invention analyzes the working principle of a lithium ionic battery and builds a power lithium ionic batterydivalent RC equivalent model, identifies the parameter models through laplace transformation and a large number of experiment data, and adopts a matlab platform to estimate the power lithium batterycharge state. The emulation result shows that the model has the good adaptability to the complex working conditions of electric automobiles, not only can the precision of SOC estimation be improved, but also the computing quantity is small, and SOC estimation is easy to achieve.

Methods for maximum scene surface temperature estimation for blades with reflective surface properties in advanced stationary gas turbines are disclosed. The approach utilizes high speed infrared imagery provided by an online monitor system using a focal plan array (FPA) for near-infrared monitoring during engine runtime up to base load. The one waveband method for temperature estimation is assumed as starting point. A lower surface emissivity and higher surface reflectance of thermal barrier coating (TBC) in near-infrared can cause systematic estimation errors. Methods using the one wave band method, with the purpose to reduce estimation errors for maximum temperatures are also disclosed. Theoretical results, data from numerical simulations, and real data from engine test are provided. A system for performing temperature estimation methods is also disclosed.

The invention discloses a monitoring device and a monitoring method for filter screen life of a purifier. The method comprises the following steps: firstly acquiring current air quality information and current wind speed information through a detection module, and then sending to a center control module, computing the rest working life of the filter screen of the purifier through the center control module according to the current air quality information and the current wind speed information, when the rest working life equals to a life limit threshold value, reminding a user of replacing the filter screen of the purifier. Since the air quality is considered in the computation of the life of the filter screen, the computed life is more close to the actual life of the filter screen. The life of the filter screen is progressively decreased per 10 sec according to the current wind speed and air quality, the life of the filter screen can be more carefully estimated, the estimation error is reduced, the computation precision is promoted, and the client loss is reduced.

Estimating remaining capacity and remaining time of a battery device during discharging of the battery device includes determining initial state of charge of the battery device, determining discharge current of the battery device, utilizing a shooting end of discharge process to determine final state of charge corresponding to the discharge current, and determining the remaining capacity and the remaining time according to the final state of charge, the method in the invention compared with the prior art, is advantaged by less influence of estimation result of remaining capacity and run-time by discharge current, smaller estimation error, and less required recursive computation amount, namely, quicker and more accurate estimation operation.

The invention relates to a battery SOC (state of charge) estimation method by utilizing a vehicle-mounted charging machine identification battery parameter. A battery model parameter is identifies through a pulse experiment which is performed by an electric automobile vehicle-mounted bidirectional charging machine on a battery, the battery capacity is recalibrated by utilizing the bidirectional charging machine to perform deep charging and discharging on the battery, extended Kalman filtering is performed then according to an updated battery parameter to estimate a state of charge (SOC) of the battery, and then an influence of a battery parameter changed, which is caused by aging, on battery SOC estimation precision is overcome. A changing battery parameter can be effectively tracked, and a selection of initial data is not depended on; the battery parameter is identified through charging and discharging which are performed by the vehicle-mounted bidirectional charging machine on the battery, and an advantage that an electric automobile is provided with a power electronic conversion device is fully used; in the whole parameter identification process, an electric energy released by the battery is grid-connected in a feedback manner, and energy loss is small; and the method is simple and practicable, and tedious operation that a traditional off-line identification battery parameter method needs to detach a battery box is prevented.

The invention discloses a fast sparse Bayesian learning based direction-of-arrival estimation method and mainly solves the problems of heavy computation and large location estimation error in the prior art. The method includes the implementation steps: (1) adopting antenna receivers to form a uniform linear array; (2) sampling space signals and computing an array covariance matrix R; (3) vectorizing R to obtain a sparse model vector y; (4) dividing space domain grids, and constructing an over-complete base phi(theta) according to the structure of the sparse model vector y; (5) establishing a sparse equation according to the sparse representation relation of the sparse model vector and the over-complete base; (6) defining a hyper-parameter vector alpha, and adopting a fast sparse Bayesian learning algorithm to solve the sparse equation; (7) drawing a magnitude spectrogram according to an optimal estimation value of alpha to obtain a direction-of-arrival value. By the method, estimation accuracy of target reconnaissance and passive location under the conditions of low signal to noise ratio and small snapshot number is improved, computational complexity is lowed, and the method can be applied to target reconnaissance and passive location.

Distortions of radio signals transmitted in data blocks in an OFDM method, the distortions being caused by transmitter- or receiver-end IQ asymmetries and by channel distortion, can be estimated and equalized by means of an iteration method. The method can be used particularly advantageously in a direct-mixing receiver.

The invention relates to a neural network compression method and a device, computer equipment and a storage medium, and relates to the technical field of neural networks. The method comprises the following steps: respectively inputting training samples into a teacher network and a student network; obtaining first network data including a first model parameter of an ith layer and a first feature map in a teacher network, and performing cross calculation on the first network data and the second network data to obtain a loss function value, and updating the second model parameter of the ith layerin the student network according to the loss function value, wherein the second network data comprises the second model parameter of the ith layer and the second feature graph. According to the method, the accuracy of the compressed neural network can be improved under the scene that the trained neural network is compressed through a small amount of training data.

The invention provides a multi-cell large-scale MIMO system user dual-antenna pilot frequency interference elimination method. Pilot frequency pollution of an uplink and interference in the data transmission process of a downlink are alleviated by considering spatial correlation and controlling the beam forming weight vector of a user side. Meanwhile, pilot frequency distribution is introduced to the method, and a pilot frequency distribution strategy based on a greedy algorithm is put forward so that influence caused by pilot frequency pollution is further alleviated through combination of a user dual-antenna device, and thus the objectives of reducing channel estimation error and enhancing system speed can be achieved.

The invention discloses a combined DOA and TOA single-station passive positioning method based on error correction. According to measurement information obtained by a receiving station of an externalradiation source radar system, by introducing an auxiliary variable, a nonlinear equation is converted into a pseudo-linear equation, a pseudo-linear model between the state of a target position and the state of an observation station position is established, an iterative least square method is adopted for estimation, a constrained total least square estimation model is constructed, a constrainedoptimization problem is converted into an unconstrained optimization problem, a Newton iteration method is used for optimization and solution, and the correlation between the auxiliary variable and the target position is used for constructing a correlation least square estimation model, so that the target positioning performance is further improved. The target positioning accuracy is improved through combined estimation of the target position and system errors. Convenience is provided for improving the target positioning performance, the complexity of error registration under the premise of ensuring the estimation performance is reduced, and good positioning performance in the case of large measurement noise is achieved.

The invention discloses a space-based phased-array radar space multi-target orbit determination method, which mainly solves the problems that the space weak target cannot be evaluated effectively and the target orbit determination precision is low in the prior art. The method comprises the following steps of: processing target echo data by a zero-setting conformal algorithm, acquiring distance prior information by utilizing a distance pulse compression principle, and segmentally processing echo signals according to the distance prior information; segmenting the echo signals according to the number of the targets, wherein each segment of data is a target signal and an adjacent unit signal; performing multi-target two-dimensional angle evaluation on each segment of data by utilizing a sum-difference multi-beam angle-measuring principle; performing coordinate conversion based on a space target tracking result and detection satellite orbit information; and performing orbit determination on different space targets by a Laplace type iterative algorithm, and improving the orbit determination precision by a least square algorithm. According to the method, the influence of strong signals on weak targets can be reduced and the parameters of the weak targets can be evaluated accurately. The method can be applied in the actual application fields of space situation awareness, orbit resource management and the like.

The invention discloses a model inverse dynamic algorithm for the extreme power of a power battery pack, and aims to avoid the influence of the state of charge (SOC) accuracy of a battery on an extreme power estimated value due to calculation of the electrodynamic potential of the battery through a mathematical model. The model inverse dynamic algorithm comprises the following steps: establishing a polarization voltage model of a single battery and a terminal voltage model of the single battery by adopting a Thevenin equivalent circuit; computing the direct-current resistance R, polarization parameter Rp and tau of the battery according to an HPPC (Hybrid Pulse Power Characterization) experiment, and establishing a corresponding relation through the SOC and a temperature; computing current EMF(t) by using a current sampled voltage value U(t) and current I(t); computing a polarization voltage Up(t+dt) after pulse time according to Up(t); calculating extreme current according to EMF(t) and Up(t+dt); comparing the extreme current with a system design required value Imax, and selecting lower current for calculating a voltage value U(t+dt) after the pulse time; and computing extreme power and charging extreme power.

The invention discloses a permanent magnet linear synchronous motor slip form control system based on a linear expansion state observer, and belongs to the technical field of linear motor control. The system comprises the following steps that: firstly, establishing the dynamic equation of a permanent magnet linear synchronous motor on a two-phase synchronous rotation orthogonal coordinate system; secondly, simplifying the dynamic equation into a special two-order integral series type mathematical model; thirdly, designing the linear expansion state observer to obtain a disturbance estimated value, and considering the estimated value in the design of a slip form control law to eliminate a chattering phenomenon; and finally, applying a Lyapunov stability theory to analyze the stability of the system. The system has the most important characteristics that the state and the disturbance of the system can be accurately estimated by the linear expansion state observer. In addition, the control system is high in robustness, and a given displacement signal can be accurately tracked. In addition, the chattering phenomenon of slip form control can be greatly improved so as to be suitable for designing the permanent magnet linear synchronous motor servo control system.

The invention discloses a channel estimation method, comprising: a. carrying out conventional channel estimation on usable pilot frequency located in usable sub-carrier scope, setting virtual channel frequency response estimation for virtual pilot frequency located in virtual carrier scope to assure smooth transition, thus acquiring channel frequency domain response estimations in all pilot frequency positions; b. carrying out inverse Fourier transform for the channel frequency domain response estimations got from step a, to get time domain response estimations; and c. cutting and interpolating zero to the time domain response estimations got from step b, and then carrying Fourier transform to get complete channel frequency domain response estimation. The invention solves the following problems in current technology: large channel estimation error, and obvious channel estimation error in sub-carrier position near virtual carrier. The inventive scheme can effectively reduce, even eliminate the effect of virtual carrier to channel estimation performance, and especially decrease the channel estimation error of sub-carrier position near virtual carrier.

The present invention relates to a spacecraft attitude fault-tolerant control method based on an iterative-learning disturbance observer. In order to solve the problem that actuator faults and external disturbances might occur during the attitude control process of the on-orbit operation of a spacecraft in the prior art, the invention provides an active fault-tolerant control method based on the iterative-learning observer. The method comprises the steps of firstly, establishing a dynamic model for the attitude control system of a spacecraft in considering the fault of an actuator in the attitude control system of the spacecraft and the external disturbance in the space of the spacecraft; secondly, estimating and designing a generalized disturbance torque of the iterative-learning disturbance observer composed of the actuator fault information and the external disturbance; finally, based on the estimated generalized interference, designing an active fault-tolerant controller. Based on the method, the stability of the attitude control system of the spacecraft, when the actuator of the spacecraft has a fault and the external disturbance exists in the space of the spacecraft, can be ensured. Meanwhile, the precision requirement of an actual control system is met. The method is strong in fault-tolerant ability and good in robustness for the external disturbance.

The invention discloses an image de-spin and stabilization method based on subarea matching and an affine model, which comprises the steps that 1, a reference frame and a current frame are subjected to image histogram equalization processing; 2, four areas distributed crosswise and an image center area are selected as matching areas of motion estimation; 3, an affine transformation model is established, and a current frame motion vector is solved; and 4, a kalman filter model is established, and motion compensation of a current frame displacement vector is achieved. In Step 2, the operand is less; a speed is high; the motion estimation capacity is favorable when translation and rotation motion coexist in an image; and an interframe estimated error is less than one pixel. According to the method, the random motion quantity of a video image can be removed with high precision; scanning motion inherent in a platform or a load is kept; and the smoothness of a dynamic image sequence is improved. The method can effectively improve the video image and inhibit random noise.

The invention provides an online estimation method of a power lead-acid battery of a special engineering vehicle. The method comprises the following steps: 1, calculating an initial residual capacity SOC0 and an effective rated capacity Qt of a battery when a battery management system is powered on; 2, during charge and discharge process of the battery, using a corrected ampere-hour integration method to calculate the residual capacity of the battery; 3, using an internal resistance method based on a battery model to correct the residual capacity; and 4, outputting an online estimation result. The method utilizes a simple and practical SOC estimation model, integrates various factors, which affect the residual capacity of the storage battery, into the battery model, improves the estimation precision of the residual capacity of the battery, and has great practical meaning.

The invention belongs to the field of digit signal processing, and provides an improved frequency estimation method and device for all-phase time-shift phase difference, capable of improving precision and robustness and lowering signal-noise ratio threshold. The frequency estimation method includes the steps of inputting M=2N-1+L samples, namely x(-N+1-L) to x(N-1), obtaining a first path phase value phi, conducting all-phase FFT for x(-N+1-L) to x(N-1), directly taking the phase of index value K=K2 at peak spectrum as a second path phase value phi2, multiplying the difference of two paths of phase value (phi-phi2) with 1 / delta omega L to obtain the initial frequency bias estimated value epsilon, conducting frequency drift compensation for the initial frequency bias estimated value epsilon to obtain the modified frequency bias estimated value ^ epsilon, and outputting the normalized frequency estimated value as described in the description. The method and apparatus are applicable to digital signal processing.

The invention discloses a large-scale MIMO channel joint estimation and feedback method based on deep learning. The method comprises the steps: performing initial channel estimation at a user side; constructing a channel estimation subnet CEnet, and minimizing the estimation error through training; constructing a channel feedback subnet; at the user side, inputting the optimized channel estimation value, and outputting compressed code words; at the base station end, inputting the code words, and outputting the reconstructed channel matrix. And the two subnets jointly form a channel estimation and feedback joint network CEFnet. A previous CSI feedback network assumes that perfect channel state information is obtained, does not consider that a channel in practice is obtained by estimation, and has errors and noise. According to the invention, a complete downlink channel estimation and feedback process is realized by constructing a channel estimation and feedback joint network CEFnet, the purpose of eliminating errors and noise is achieved by using a brand new network architecture, and the reconstruction precision is improved while the feedback overhead is reduced.

The invention relates to a mobile communication technology, in particular to a channel estimation method of a LTE-A system. The channel estimation method is that reference signals special for terminals carried by resource blocks are utilized to perform preliminary LS channel estimation to subcarriers which the reference signals belong to at the corresponding positions under the condition of TM9-mode multiple-layer beam forming, an expectation-maximization (EM) iterative algorithm is adopted to perform accuracy processing of channel estimation values at the subcarrier positions distributed with the reference signals so as to decrease estimation errors, and then a frequency-domain LMMSE interpolation algorithm and a time-domain duplication interpolation algorithm are adopted to process channel estimation values of the subcarriers where the reference signals are not distributed. By the adoption of the channel estimation method, the channel estimation is achieved by utilizing collaboration of multiple received reference signals and local reference signals, the trouble that various layers of reference signals are extracted from mixed reference signals is avoided, processing flows are decreased, and the complexity degree of the subsequent processing and control is also reduced.

The invention discloses a direction of arrival angle estimation method based on Sparse Bayesian learning. The direction of arrival angle estimation method mainly solves the problems in the prior art that the computation burden is heavy, the performance of the coherent signal source process is poor, and the errors in the passive location estimation are big. The direction of arrival angle estimation method comprises the following steps: (1) using an antenna receiver to form a uniform linear array, and sampling space signals to obtain observation data, (2) converting the observation data into real values and calculating a covariance matrix, (3) carrying out the mesh generation on airspace, and constructing a real value over-complete base, (4) establishing a sparse matrix equation according to the sparse presentation relationship of the covariance matrix and the over-complete base, (5) obtaining a most sparse solution of an unknown matrix variance through solving a matrix equation by employing the Sparse Bayesian learning, (6) drawing an amplitude spectrogram based on the one-to-one corresponding relation between the spares solution and the space angle, and obtaining the direction of arrival angle degree. According to the direction of arrival angle estimation method, the passive direction-determination calculating speed and the estimation performance on the signal direction angle when in fast and low number of beats are improved. The direction of arrival angle estimation method is applicable to the target reconnaissance and the passive direction-determination.

The invention provides an attitude angle direct resolving method based on a global position system (GPS) carrier wave double-difference equation. The attitude angle direct resolving method mainly comprises building a carrier wave double-difference equation, guiding carrier attitude information into the carrier wave double-difference equation and solving an attitude angle by utilizing nonlinear least squares. The attitude angle direct resolving method can effectively reduce or eliminate short base line public errors, achieves high-precision resolving, can directly resolve the attitude angle by leading the attitude angle information into the double-difference equation, greatly reduces intermediate evaluated errors, evaluates the attitude angle by utilizing the nonlinear least squares method, improves system resolving speed, finishes real-time resolving of the attitude information and is more suitable for real-time carrier attitude measuring.

The present invention discloses a construction method for a compound thrombus clearing preparation bioactivity chromatography finger print. The construction method comprises the following steps: (1) preparing a compound thrombus clearing preparation test solution, precisely sucking 10 mul of the compound thrombus clearing preparation test solution, and injecting into a double-pump dual-gradient high performance liquid chromatograph to obtain a compound thrombus clearing preparation HPLC finger print; (2) preparing a mixed reference substance solution, precisely sucking 10 mul of the reference substance solution, and injecting into the double-pump dual-gradient high performance liquid chromatograph to obtain a reference substance HPLC finger print; (3) adopting a reference substance control method, and adopting an RRLC / MS / MS technical method to prove 21 chemical components in the finger print; and (4) analyzing correlation between the 21 chemical components and bioactivity difference, clarifying bioactivity component groups, and establishing a compound thrombus clearing preparation bioactivity chromatography finger print. According to the present invention, the compound thrombus clearing preparation HPLC finger print and the bioactivity component groups obtained from chromatograph efficiency analysis are combined to construct the compound thrombus clearing preparation bioactivity chromatography finger print so as to completely monitor safeties, effectiveness and quality uniformities of raw material herbs, semi-finished products and finished products.

The invention discloses a target positioning and tracking method for a wireless sensor network. The method comprises the following steps: at any positioning time, estimating a position of a target according to measurement information of a sensor node, building a learning region covering the estimated position of the target, selecting an arbitrary quantity of position points in the learning region, obtaining a decision function by use of a polynomial kernel function and a mapping relationship of the distance vector from an approaching position point of an epsilon-support vector machine for regression to the sensor node and a coordinate of the position point; and obtaining an estimated value of the position of the target by inputting a distance vector from the sensor node to the target into the decision function and transmitting an estimated value of the position of the target to a base station which then fits the historical data of the position of the target and upgrades the motion patch of the target to complete target tracking. The method obviously reduces the influence of measurement errors of the sensor node on the target positioning and tracking and improves the accuracy of target tracking.