113 results about "Optimal estimation" patented technology

The embodiment of the invention provides a computer communication method and system based on the Internet of Things, and the method comprises the steps: constructing a multi-level system architecture, carrying out the preprocessing and feature extraction of an original data flow, recognizing the data characteristics through a time sequence analysis method, and constructing a dynamic data model; deploying a monitoring agent at a transmission node to collect network performance indexes in real time, and constructing a network quality evaluation model; for an incomplete sensor data flow, prior probability distribution is constructed based on a dynamic data model and a network quality grade, and an optimal estimation value of missing data is calculated by adopting a Bayesian reasoning framework and an iterative algorithm; establishing a mapping relation between a network state and an optimal parameter through reinforcement learning to realize self-adaptive adjustment and optimization; and grading the data according to reliability, extracting high-reliability data points as anchor points, designing an iterative refinement algorithm to realize information propagation, and fusing to obtain a complete sensor data stream. According to the method, the problems of poor data recovery accuracy, static parameter configuration and insufficient adaptability in a complex network environment are solved.

The invention relates to the technical field of data filtering and data fusion, in particular to an improved weighted Kalman data fusion algorithm. The algorithm comprises the following steps: establishing a state equation, calculating a state prediction value at the current moment according to state estimation of current / voltage data at the previous moment to become priori, and calculating an error covariance matrix at the same time; obtaining a prediction covariance matrix at the current moment through matrix operation; calculating an observation noise covariance matrix; calculating to obtain a Kalman gain; calculating to obtain an updated optimal state estimation value; calculating a current optimal estimation value and an optimal estimation value covariance matrix; calculating a weighting factor, and compensating and updating the covariance matrix of the current / voltage data; calculating to obtain an updated covariance matrix; and then loop iteration is carried out on the steps, so that the estimation precision of the system state is gradually improved. The fusion algorithm provided by the invention can improve the data precision and accuracy.

The invention provides a self-adaptive robust vehicle navigation method and device based on LSTM assistance, and the method comprises the steps: calculating a process noise scale factor through process noise covariance estimation, and dynamically adjusting a process noise covariance matrix Q in a Kalman filter; executing a prediction process of Kalman filtering to obtain a one-step prediction state value and a state prediction covariance matrix; calculating a robust factor and a mahalanobis distance adaptive factor based on the prediction residual vector, and performing expansion adjustment on the prior measurement noise covariance matrix R and the predicted state prediction covariance matrix P; executing an updating process of Kalman filtering to obtain a state optimal estimation and a covariance matrix at the current moment; and when the GNSS loses lock, pseudo GNSS extended Kalman filtering is carried out by using a pseudo GNSS measurement value generated by the LSTM neural network and combining a process noise covariance estimation method. The method can better adapt to the dynamic characteristics of the navigation system in different environments, and provides powerful support for the stability and reliability of the combined navigation system.

The invention provides a carbon emission calculation method based on adaptive Kalman filtering. According to the carbon emission calculation method based on adaptive Kalman filtering, a data matrix is generated by acquiring multi-source data, and a unified data basis is provided for subsequent calculation; a dynamic system model and an error measurement matrix are initialized, a state transition model indicates a carbon emission evolution law, an observation model establishes a mapping relation between multi-source data and carbon emission, and the error measurement matrix dynamically indicates a model effect and adaptively adjusts calculation parameters to ensure the adaptability of the model to the dynamic change of the system; a carbon emission prediction parameter and an error measurement matrix are calculated in real time through adaptive Kalman filtering, a prediction value is corrected through an observation parameter, an optimal estimation parameter and an observation residual error are obtained, meanwhile, the error measurement matrix is updated in real time, and dynamic fusion and noise suppression of multi-source data are achieved through the closed-loop process. And the precision and the real-time performance of carbon emission calculation are improved.

The invention discloses an atmosphere and ocean integrated atmosphere temperature and humidity profile inversion method based on multiple sources and multiple frequency bands. The method comprises the following steps: S1, acquiring multi-source collaborative observation data, and preprocessing the acquired data; s2, constructing an integrated state vector and coupling forward model, and obtaining an integrated state vector X; s3, performing optimal estimation based on the preprocessed data and the integrated state vector X, and performing joint inversion to realize integrated inversion solution; and S4, outputting an inversion result and theoretical uncertainty output. According to the method, integrated cooperative inversion is carried out through a physical model and an optimal estimation theory, and the inversion precision of the atmosphere temperature and humidity profile in the clear sky and in the presence of clouds, especially in the air above the ocean, is remarkably improved.

The invention discloses a self-adaptive integrated navigation method based on geometric accuracy factors, which belongs to the technical field of underwater navigation and positioning, is used for underwater navigation and positioning, and comprises the following steps: initializing an inertial navigation system and an integrated navigation filter, and setting a state vector and a noise covariance matrix; predicted navigation parameters of the recursive carrier are mechanically arranged through inertial navigation, and the state transition matrix is used for state prediction; and dynamically adjusting an observation noise covariance matrix according to the geometric precision factor value, further calculating a Kalman gain, performing optimal estimation and correction on a prediction state by fusing acoustic observation information, and finally outputting a high-precision carrier position, speed and attitude. According to the method, the statistical characteristics of observation noise are dynamically remodeled by calculating and feeding back geometric precision factor values in real time, so that the Kalman filter has the capabilities of knowing the own geometric situation and adjusting the trust degree of each information source, and the global optimal navigation precision and reliability are realized under any motion track.

A nuclear power plant passive system reliability analysis method based on meta-model adaptive sampling comprises the following specific steps: 1, extracting a sample to establish a model, obtaining a response value through an optimal estimation program, and training an initial response surface; 2, extracting a sample, mapping by using a response surface, and selecting a response value as an intermediate event critical value; 3, selecting a design point from the samples within the critical value range as a new sampling center, generating a new sample, supplementing training data, and constructing a new response surface; 4, obtaining a sample obeying important sampling probability density function distribution by using new response surface mapping, obtaining sample points falling in a critical value range, and re-selecting a critical value; and 5, repeating the steps 3 and 4 until the critical value is less than 0, ending hierarchical calculation, and obtaining an estimated value of the failure probability. The method aims at reliability analysis and development of the passive system of the nuclear power plant, the precision of the response surface model is improved through the adaptive sampling algorithm, the calculation cost is low, and the calculation precision is high.

The invention discloses a vegetation carbon density and carbon reserve estimation method and device based on machine learning, and relates to the field of vegetation carbon density and carbon reserve accounting, the vegetation carbon density and carbon reserve estimation method based on machine learning mainly comprises the following steps: obtaining land cover and vegetation type data of a research area; setting a standard sample plot in the research area and investigating to obtain overground, earth surface and underground vegetation carbon density and carbon reserve data of the sample plot; performing preprocessing and information extraction on the land cover data of the research area to obtain remote sensing ecological factors of the sample plot; according to the sample plot remote sensing ecological factors and sample plot carbon density measured data, vegetation carbon density model construction and optimization are carried out, and an optimal estimation model is obtained; and according to the vegetation type and the vegetation area, utilizing the optimal estimation model to obtain the vegetation total carbon reserve in the research area. According to the vegetation carbon density and carbon reserve estimation method and device based on machine learning provided by the invention, rapid, dynamic and accurate estimation of multi-element vegetation carbon density and carbon reserve of regional scale forest grass wet and rough vegetation can be realized.

The invention relates to the technical field of communication, and discloses a measurement and control communication parameter transmission state monitoring system, which comprises a data acquisition and adaptation module which is responsible for acquiring an original parameter data stream by measurement and control equipment, performing unified formatting processing on the original parameter data stream and then sending the original parameter data stream to a state optimal estimation module. A technical scheme of constructing a state space model at an edge end, performing optimal state estimation on original measurement and control parameters in combination with a Kalman filter, and performing statistical distribution deviation quantification on a filtered residual sequence by using KL divergence is adopted, a system high-fidelity operation state is extracted from strong noise background data, and progressive weak abnormity is early warned in advance and accurately. Compared with a scheme of carrying out fixed or simple statistical threshold alarm on an original measurement value, the problems that state misjudgment is caused by measurement noise interference, progressive performance degradation cannot be effectively sensed, fault discovery is lagged and the early warning capability is insufficient are solved.

The invention discloses a thin pancake machine heating temperature data analysis processing method and system, and relates to the technical field of data analysis, in the step S2, a temperature distribution model is fitted by using a temperature rise data set, and statistical characteristics of temperature in each monitoring period are accurately reflected through calculation of an optimal estimation parameter. A plurality of groups of temperature intervals are divided according to an equal probability mode through probability density analysis, a high-density area in temperature distribution can be effectively identified, the probability distribution-based division method ensures that the cumulative probability of each temperature interval is equal, and S3, based on the divided temperature intervals, a target temperature is set and the heating power is dynamically optimized, so that the heating efficiency is improved. The heating power can be monitored and adjusted in real time in the process, so that the heating process of the pancake machine is kept within the target temperature range, and the phenomenon of uneven heating caused by temperature fluctuation is reduced.

The invention discloses an engineering measurement error dynamic correction method based on multi-sensor data fusion, and relates to the technical field of engineering measurement. Three error sources of sensor physical characteristics, environmental interference and manual operation are defined as estimable state variables, and differentiation processing is performed according to different characteristics of errors: a dynamic model is established to embed the sensor and environmental errors into a state space, so that real-time compensation based on the model is realized; according to the scheme, a gross error recognition mechanism based on data-driven statistical characteristics is established, real-time diagnosis and processing are carried out on personal errors, joint optimal estimation is carried out on unified state vectors by adopting a recursive estimation algorithm, and high-precision corrected physical quantities and estimated values of all error states are synchronously output. The measurement precision and reliability are obviously improved; by means of robust processing of gross errors, stability of the system in a non-ideal environment and online self-diagnosis and health monitoring of the measurement system are enhanced.

The invention relates to the technical field of stable liquid adding, and particularly discloses a stable liquid adding integrated system and method for a test solution. The solution temperature sudden change working condition is recognized in real time, and the self-adaptive control process is triggered; through four-dimensional state space modeling and optimal estimation, basic data are fused, multi-physics field coupling errors are suppressed, and high-precision state estimation is provided; the viscosity compensation coefficient of each fluid type is calculated, parameters in the liquid adding process are dynamically adjusted, and flow resistance fluctuation caused by viscosity change is compensated, so that the experimental solution can be stably added; through a four-dimensional dynamic control model, a fluid type differentiation compensation strategy and a self-adaptive PID parameter adjustment technology, and in combination with a feedforward-feedback collaborative architecture and a high-precision data acquisition system, precise and stable control of a test solution adding process under a multi-physics coupling working condition is realized; the adaptive bottleneck of a traditional liquid adding system to solution temperature mutation, fluid characteristic difference and complex interference is broken through.

The invention relates to the technical field of computers, and discloses an industrial object-oriented Gaussian approximation filtering method for a nonlinear system. Mathematical modeling is carried out on a physical system to be subjected to state estimation, and a nonlinear state space model corresponding to the physical system is obtained; matching the first Gaussian distribution of the system state at the time t to a given distribution through a moment matching operation based on the non-linear state-space model to determine a predictive estimate and a predictive estimate error of the system state at the time t; acquiring a system observation value at the moment t; iteratively updating the optimal estimation and the optimal estimation error of the system state at the moment t based on a natural gradient descent method and a Fisher information matrix associated with the second Gaussian distribution of the system state at the moment t, and obtaining the optimal estimation and the optimal estimation error at the moment t until an iteration stop condition is met; linearization errors can be avoided, and more accurate system state estimation of a nonlinear system is provided.

The invention relates to the technical field of data processing, in particular to an electric power project data fusion method and system. The method comprises the following steps: collecting and preprocessing multi-dimensional time sequence data of a distributed power supply and an energy storage system; extracting features of the preprocessed data; an adaptive forgetting factor is introduced to carry out adaptive Kalman filtering processing on the characteristic data, the adaptive forgetting factor combines a current error, an error change trend and a historical error weighted sum difference value, and a prediction covariance matrix and a measurement noise covariance matrix are dynamically adjusted, so that the response capability of filtering to system mutation is improved; and fusing the optimal estimation state of each feature by adopting a D-S evidence theory so as to accurately judge the operation state of the electric power project. According to the method, the problem that estimation is lagged when the state is suddenly changed in traditional Kalman filtering is effectively solved, the accuracy and the reliability of judging the operation state of the electric power project are improved, and an effective guarantee is provided for stable operation of a micro-grid system.

The application relates to a SINS large misalignment angle fast alignment method based on a Lie group and a factor graph, and belongs to the technical field of large misalignment angle alignment, and comprises the following steps: S1, defining system states under a Lie group framework; S2, constructing a factor graph model of the SINS / GNSS large misalignment angle fast alignment; S3, constructing an SINS factor cost function under the Lie group framework; S4, constructing a GNSS factor cost function under the Lie group framework; S5, optimizing the system states under the Lie group framework by using the factor graph model, the SINS factor cost function and the GNSS factor cost function, and obtaining optimal estimation of the system states; and S6, obtaining a theoretical value of a Lie group matrix containing elements related to an attitude, a speed and a position according to the optimal estimation of the system states, that is, completing the SINS large misalignment angle fast alignment method. The application has the advantages of strong robustness, good precision, simple operation, fast convergence, low cost, high alignment precision and good practicability.

The UE may measure a metric for a first subset of PO beams of at least one subset of PO beams comprising a plurality of PO beams from a network node, each subset of PO beams being configured to form a PO pattern, and the first subset of PO beams being associated with a first set of narrower RX beams of the UE; estimating metrics for the first set of narrower Rx beams using an ML model based on the metrics for the subset of PO beams; and identifying at least one optimal Rx beam from the first set of narrower Rx beams, the at least one optimal Rx beam associated with an optimal estimation metric in the first set of narrower Rx beams.

The invention provides a ballistic extrapolation method for estimating an optimal ballistic coefficient based on RANSAC (Random Sample Consensus). The method comprises the following steps: firstly, acquiring part of ballistic trajectory data of a flying projectile by using the emplacement reconnaissance and calibration radar, and carrying out secondary filtering on the part of ballistic trajectory data so as to reduce a measurement error generated in a radar measurement process and reduce a ballistic extrapolation distance; then, an RANSAC (Random Sample Consensus) algorithm is adopted to calculate different data parameter models for different ballistic coefficients and trace point data, and an optimal model is selected by utilizing a root-mean-square error, so that an optimal estimated ballistic coefficient is obtained; and finally, extrapolation is carried out by using the optimal estimation ballistic coefficient so as to improve the emplacement extrapolation precision. According to the method, the trajectory extrapolation precision can be greatly improved, and the shot point of emplacement extrapolation is more accurate.

The application discloses a molten silicon liquid level estimation method based on a hybrid adaptive resampling particle filter, and comprises the following steps: firstly, processing laser spot images collected by a CCD sensor to obtain observation data of the liquid level; then, establishing a system state equation of the molten silicon liquid level according to a kinematic principle to describe the movement of the liquid level; performing state estimation on a laser centroid vertical coordinate by using a particle filter algorithm, and obtaining a new particle set by using a hybrid adaptive resampling method; after re-normalizing all particle weights, outputting an optimal estimation value; and finally, smoothing the filtered data by using a moving weighted average method, and the smoothed result is the estimation value of the molten silicon liquid level. The application solves the problem that in the prior art, the variance of Gaussian variation is too large, particles jump out of a sampling range, and it is difficult to accurately estimate a real liquid level.

The invention provides an active and passive microwave joint inversion method and system combined with Bayesian probability inversion, and the method comprises the steps: carrying out the sampling in a priori numerical range of a to-be-inverted parameter, obtaining an initial guess value, and carrying out the simulation based on the initial guess value, and obtaining active simulation data. And performing active inversion by combining the active simulation data and the active microwave observation data, and determining an effective roughness parameter required by passive inversion based on the roughness parameter obtained by the active inversion and a pre-constructed relation function. And performing passive inversion by combining the effective roughness parameter, the initial guess value of the soil moisture and the passive microwave observation data. The active inversion result and the passive inversion result respectively comprise an optimal estimation value and an uncertainty quantitative index of the to-be-inverted parameter. And obtaining a joint inversion result by combining the active inversion result and the passive inversion result. According to the scheme, active and passive microwave observation is combined to accurately estimate the soil moisture and quantify the uncertainty in the inversion algorithm, and the soil moisture estimation precision and reliability are improved.

The application discloses a kind of gas insulated switchgear gas chamber pressure leakage degree judging method and system, applied to insulating switchgear gas chamber detection technical field, method includes first obtaining the pressure value of the gas chamber to be evaluated in preset time period, constructs pressure value sequence, and then the sequence is weighted average processing, obtains first final pressure value sequence, again to first final pressure value sequence Pressure mutation identification, locates pressure mutation interval and removes it, obtains second final pressure value sequence.Then, construct pressure state space model, input model to predict second final pressure value sequence, obtain optimal estimation pressure value sequence, calculate pressure decay rate according to optimal estimation pressure value sequence, and the pressure decay rate is segmented linear transformation, finally output gas chamber pressure leakage severity value.Through the above method, the accuracy of pressure leakage evaluation is effectively improved.

The invention discloses a gas well production state estimation method and system based on particle filtering. The method specifically comprises the following steps: S1, collecting production data of different types of gas wells; s2, visual curve drawing is carried out according to the production data of different types of gas wells; and S3, outputting an estimated gas well state through the constructed particle filtering prediction model based on the acquired data of gas well production. Dynamic estimation of gas well parameters is achieved based on particle filtering, the particle filtering serves as a suboptimal estimation framework, and when the number of particles is large enough, the particle filtering can approach the optimal estimation result; meanwhile, compared with Kalman filtering, the particle filtering has the advantages that the particle filtering can also approach optimal estimation for a nonlinear and non-Gaussian process; the gas well state estimation accuracy is improved; meanwhile, the production trend and the occurrence of abnormal conditions are judged through the estimated gas well state, and the management level and safety are improved.

The invention provides a filtering method for optimal estimation of a motion state of an unmanned aerial vehicle under an accelerometer fault, and relates to the technical field of unmanned aerial vehicle flight control. The method comprises the following steps: establishing a kinematic model and a kinetic model of a target unmanned aerial vehicle; modeling an accelerometer fault into a random walk model, wherein values of diagonal elements of a covariance matrix of the random walk model reflect the speed of fault change; on the basis of the kinematic model and the dynamical model, considering the fault of the accelerometer and the measurement noise of the inertial measurement unit, and establishing a continuous time linear state filtering model; identifying a covariance matrix of the random walk model, designing a two-step Kalman filter, and taking the information of a fault-free filter as the input of a fault filter; and a final state estimation value and a final fault estimation value are obtained through output of the two-step Kalman filter. The method has good stability for the aircraft with the characteristics of strong nonlinearity and high dimension.

The application discloses a kind of unknown distribution's laser interferometer measurement result's uncertainty evaluation method, is used to the measurement result of non-Gaussian and distribution type unknown for uncertainty evaluation;The steps of this method include: 1 using beta distribution method to express measurement result;2 the parameter of beta distribution is regarded as to be estimated parameter, and state space model is established using Bayesian statistical method;3 determine the prior distribution of beta distribution parameter, and the beta distribution parameter of measurement result is estimated recursively based on particle filtering method;4 according to distribution parameter estimated value, the distribution type of interferometer measurement result and its uncertainty are obtained.The method of the application can represent the multiple distribution types of laser interferometer measurement result by beta distribution, so as to solve the optimal estimation and uncertainty evaluation problem of the measurement result of distribution type unknown.

The invention discloses a multi-rate multi-sensor information fusion method and system and a storage medium, and the method comprises the steps: obtaining an observable subsystem of each local sensor through observability decomposition; designing a local Kalman filter in the observable subsystem, and executing Kalman filtering on the observable subsystem at a sampling moment; obtaining an estimation value of a local sensor and an estimation error covariance matrix to calculate a fusion weight; and obtaining a fusion estimation value according to the fusion weight and the optimal estimation value of the local sensor. For a multi-rate system with a local unobservable sensor, a distributed fusion estimation framework is provided, and the problems of asynchronous data alignment and partial observability are solved. Through observability decomposition, a stable Kalman filter is designed in an observable subspace of each sensor to prevent divergence; realizing heterogeneous sensor estimation synchronization based on a dynamic alignment mechanism of a state transition matrix; covariance cross fusion ensures consistency and robustness.

The present invention provides a method for estimating downward shortwave radiation from a cloud-space surface, comprising: S1. collecting data and constructing a sample data set; S2. constructing a multi-task learning model with physical constraints to obtain estimated values ​​of two key parameters in the cloud-space radiation estimation model; S3. based on the cloud-space radiation estimation method in the physical model FARMS, combined with the key parameters estimated by the deep learning model, obtaining the estimated value of downward shortwave radiation from the cloud-space surface; S4. based on the estimated value of downward shortwave radiation from the cloud-space surface and the ground observation value, calculating the loss function, adjusting the parameters of the multi-task learning model with physical constraints by back propagation of the loss value, and training to obtain the optimal cloud-space surface downward shortwave radiation estimation model; and S5. based on the optimal cloud-space surface downward shortwave radiation estimation model obtained by training, estimating the estimated value of downward shortwave radiation from the cloud-space surface in the target area. The method of the present invention can improve the estimation accuracy of downward shortwave radiation from the surface under the cloud-space, and has good stability and interpretability.

The invention relates to the technical field of big data resource service optimal estimation, and discloses a noise covariance estimation method with self-adaptability and robustness and application thereof, and the method comprises the following steps: S1, obtaining an initial state noise covariance matrix Q and an observation noise covariance matrix R; s2, constructing an innovation state space model according to the initial state noise covariance matrix Q and the observation noise covariance matrix R, and representing the innovation state space model as the initial state noise covariance matrix Q and the observation noise covariance matrix R; s3, constructing a correlation function coefficient matrix of the ALS, and calculating a state space model of the information through a chi-square test strategy to output test statistics of the information; and if the test statistics of the information is not less than a first threshold value, representing that the state space model of the information contains two errors, and executing a first error correction strategy. According to the method, the filtering accuracy can be ensured under the condition that the calculation amount is reduced.

The application discloses a two-dimensional direction of arrival estimation method based on L-array prior waveform signal source, comprising the following steps: solving the two-dimensional direction of arrival angle of the prior waveform based on a first convex optimization function to solve the underdetermined equation insoluble problem generated when solving the two-dimensional direction of arrival angle according to an estimated angle vector model, and obtaining an initial estimated value of the two-dimensional direction of arrival angle; solving the two-dimensional direction of arrival angle of the prior waveform based on a second convex optimization function to solve the off-lattice phenomenon generated when executing the above step, and obtaining an optimal estimated value of the two-dimensional direction of arrival angle; determining the complex amplitude estimated value of each group of two-dimensional direction of arrival angle according to the optimal estimated value; and matching the prior waveform corresponding to the two-dimensional direction of arrival angle according to the complex amplitude estimated value, and obtaining the optimal estimated value of the two-dimensional direction of arrival angle of each prior waveform. The application has a smaller calculation complexity and can be applied to a scene with a higher real-time requirement.

The invention provides a bidirectional clock synchronization method based on an LQG control strategy, and relates to the technical field of wireless communication. Firstly, a state equation of master and slave clock nodes is constructed, clock skew of the master and slave clock nodes is defined, and a process noise covariance matrix of the master and slave clock nodes is established. And obtaining a corresponding timestamp based on bidirectional communication of the master clock node and the slave clock node. And constructing an observation equation of the master and slave clock nodes based on the timestamps. Based on the observation equation, the process noise covariance matrix and the state equation, state prediction and state updating of the master and slave clock nodes are carried out, and the optimal estimation value of the state of the master and slave clock nodes is obtained. And calculating a control value of an LQR controller based on the optimal estimated values of the states of the master and slave clock nodes, and adjusting the frequency of the slave clock nodes based on the control value to realize synchronization of the master clock nodes and the slave clock nodes. According to the clock synchronization method provided by the invention, the precision and robustness of clock synchronization are improved, and the synchronization requirement of a wireless communication network is met.

The invention discloses a cross-scale numerical simulation and feedback analysis method for a seepage field. The method comprises the following steps: S1, establishing a numerical calculation model for cross-scale analysis of the seepage field; s2, determining and initializing seepage parameters to be inverted, selecting observation points and constructing a target function; s3, carrying out seepage cross-scale numerical calculation by utilizing the numerical calculation model to obtain a simulation result of a seepage field; s4, according to the simulation result of the seepage field and the observation information of the actual seepage field, calculating the value of an objective function and a Jacobian matrix; step S5, updating the seepage parameters to be inverted by using a Levenberg-Marquardt optimization method; and S6, outputting the optimal estimation of the to-be-inverted seepage parameters and a seepage field calculation result when a convergence condition is satisfied. Based on the method, the accuracy of the cross-scale numerical simulation and feedback analysis result of the seepage field is improved.

The application discloses an estimation method of an unknown failure tumbling target angular velocity based on an electromagnetic induction torque, which is composed of the following steps: obtaining historical data of the unknown failure tumbling target, wherein the historical data comprises angular velocity historical data and electromagnetic induction torque historical data; performing dimension increasing on the historical data through nonlinear transformation to obtain dimension-increased data; performing dynamic mode decomposition on the dimension-increased data to obtain a system matrix, an output matrix and a prediction matrix; constructing a linear predictor model of the unknown failure tumbling target in the electromagnetic despinning process according to the system matrix, the output matrix and the prediction matrix; and solving the linear predictor model as a constraint condition of an optimal estimation problem to obtain a real-time accurate estimation value of the angular velocity of the unknown failure tumbling target. In the electromagnetic despinning process of a close-range space target, the application realizes real-time accurate estimation of the target angular velocity by using electromagnetic despinning torque data, thereby solving the problem that the target angular velocity is difficult to measure in a close-range despinning task.