45 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 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.

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 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 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 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 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 provides a multi-sensor target fusion method and system based on sequential filtering, and belongs to the technical field of intelligent driving, and the method comprises the steps: obtaining a fusion track of a plurality of target sensors, and calculating a track state at a current moment according to a track state at a previous moment; adopting a greedy algorithm to respectively match the plurality of target sensors with the current time track; and updating the track state by adopting sequential filtering according to the matched measurement value. According to the method, the greedy algorithm is adopted for matching, the time complexity is far smaller than that of a KM algorithm and the like, but an approximate global optimal matching result can be achieved in most cases, and the calculation amount can be reduced to a great extent. According to the method, sequential filtering is adopted to update the flight path state, the method serves as a recursive filtering algorithm, measurement data are comprehensively considered, meanwhile, the method is easy to implement, optimal estimation of the system state can be provided based on the minimum mean square error criterion, and in addition, filtering parameters and measurement weight values can be adjusted according to the actual situation.

The invention relates to the technical field of millimeter wave radar signal processing, in particular to a course solving method and system through optimal estimation of the amount of motion among multiple frames and application of the course solving method and system. S2, assuming a uniform motion model; s3, constructing a cost function; and S4, solving the optimal displacement. According to the invention, jitter errors caused by dependence on single-point amplitude in a traditional method can be avoided; the estimation robustness is improved by using the point cloud information of the whole frame; constructing a point cloud distance sum cost function to directly reflect the inter-frame matching degree; the method is easy to implement, high in calculation efficiency and suitable for a real-time system.

The invention relates to an SF6 gas density relay verification signal anti-jitter processing method and system, and the method comprises the steps: firstly collecting a switching value signal of a relay contact in verification in real time, and obtaining an original sequence containing jitter noise; then, a Kalman filtering algorithm is initialized, and meanwhile, a snow ablation optimizer (SAO) is configured; thirdly, constructing a fitness function containing smoothness, real-time performance and accuracy indexes, and taking minimization of the fitness function as a target to drive a snow ablation optimizer to optimize to obtain an optimal covariance matrix; the optimal parameters are injected into Kalman filtering, the state is reset, original signals are input point by point, and anti-jitter signals are output through recursive optimal estimation; and finally, judging and identifying a contact action moment through a threshold value, and recording a corresponding pressure / density value to finish verification. Compared with the prior art, the method has the advantages of being high in anti-jitter adaptability and universality, accurate in verification result, intelligent in verification process and the like.

The invention discloses a multi-target tracking method and system based on tandem joint probability data association and variational Bayesian filtering, electronic equipment and a medium, and the method comprises the following steps: 1, predicting each target, and calculating the interconnection probability between each measurement and the target; step 2, generating equivalent fusion measurement and equivalent covariance for each target; step 3, performing dynamic correction on the fusion measurement noise covariance of each target; 4, updating and iterating each target by using Kalman filtering, and returning to the step 3 if the number of iterations is less than the set number of iterations; otherwise, storing the optimal estimation value and the posterior error covariance of each target. According to the method, the calculation complexity and the resource consumption are remarkably reduced, and meanwhile, the multi-target state estimation precision and the system robustness are synchronously improved.

The invention discloses a target state determination method and device, and relates to the technical field of automatic driving. A specific embodiment of the method comprises the steps of obtaining a detection result of a detector on a first type of motion features of a target at the current moment, wherein the detection result comprises a detection state value at the current moment and a detection score representing detection reliability; adjusting an initial value of the measurement noise data according to the detection score to obtain a current value of the measurement noise data; obtaining a state optimal estimation value and a state covariance optimal estimation value of the second type of motion features of the target at the previous moment, and determining a Kalman coefficient at the current moment according to the state covariance optimal estimation value and the current value of the measurement noise data; and determining the optimal state estimation value of the second type of motion features of the target at the current moment by using the optimal state estimation value, the Kalman coefficient at the current moment and the detection state value at the current moment. According to the embodiment, the calculation accuracy of the Kalman filtering algorithm can be improved.

This invention provides a method and system for measuring atmospheric data using integrated optical sensing, comprising: real-time acquisition of surface pressure distribution and surface recovery temperature using a FADS subsystem, and inversion of Mach number, static pressure, and attack angle parameters; periodic laser emission using a MOADS subsystem, and analysis of atmospheric parameters such as air velocity, attack angle, and density through backscattering spectroscopy; independent Kalman filters running on the FADS and MOADS subsystems, performing local optimal estimations using aerodynamic equations and observation equations; employing a federated Kalman filtering algorithm, dynamically adjusting the weights based on the residual covariance matrix of the two subsystems, and jointly solving the pressure, temperature data, and optical signals based on the adjusted weights, outputting Mach number, attack angle, total / static pressure, total / static temperature, and atmospheric density. The technical solution of this invention addresses the technical problems of high cost and limited environmental adaptability associated with using FADS or MOADS systems alone in existing technologies.

The application discloses a method for identifying thermophysical parameters based on prior distribution regularization, which comprises the following steps: for a heat calibration test of heatproof material, based on the temperature observation data and the identified thermophysical parameters, a covariance matrix of the identified thermophysical parameters is calculated through an information matrix, then the optimal estimation and the covariance matrix of the identified thermophysical parameters are combined to obtain a thermophysical parameter distribution of the heat calibration test, and the thermophysical parameter distribution is taken as a prior distribution; a new batch of heatproof material is tested to obtain a new group of temperature observation data, the prior distribution is combined with the new test data to form a new objective function; the new test data comprises the temperature observation data; and according to the new objective function, the optimal estimation value of the to-be-identified thermophysical parameters is obtained. The application improves the identification precision and accuracy of the thermophysical parameters.

The application relates to a Beidou / Galileo system fusion-based satellite-based precise timing method and system. Observation data, broadcast ephemeris data and correction numbers broadcast by a Beidou three-satellite system and a Galileo satellite system are respectively received by a receiver, two different systems are fused for timing, system clock differences between the two satellite systems are respectively taken as constants, white noises and random walks for construction of an estimation model, a PPP fusion model of the two satellite systems is constructed, each estimation model is brought into the fusion model for calculation, the estimation model corresponding to the best timing precision is taken as an optimal estimation model for correction of the system clock difference, other satellite data is corrected, receiver clock difference parameters are obtained, the parameters are used for clock taming, and satellite-based precise timing is completed. The method has higher timing precision and a wider application range.

Provided are a direction of arrival (DOA) estimation method and system for a sparse array based on Vandermonde decomposition reconstruction, relating to the technical field of array signal processing. The method includes: constructing a covariance matrix completion optimization model based on sparse array signals and uniform linear array signals, and performing Vandermonde decomposition by using characteristics of a uniform linear array; introducing a nuclear norm to optimize a rank function in the model, and updating the covariance matrix completion optimization model; and introducing an auxiliary variable to transform the model into a solvable optimization problem, and solving the problem by an alternating direction multiplier method to obtain an optimal estimation value. The DOA is estimated using a root multiple signal classification algorithm.

The invention provides an aerosol and gas profile inversion method and system based on MAX-DOAS, the method comprises a profile inversion step and a result monitoring step, the profile inversion step is used for double-layer MCMC profile inversion, the first layer uses an O4 inclined column concentration to invert an aerosol profile, and the second layer uses an O < 4 > inclined column concentration to invert a gas profile. The second layer of fixed aerosol information is subjected to gas concentration profile conditional probability inversion so as to obtain a profile inversion result, and the result monitoring step is used for providing quality control indexes, monitoring the inversion progress in real time and diagnosing abnormity so as to ensure the correctness of the result. According to the method, the aerosol resolution is improved to 100 m from 200 m, the trace gas resolution is improved to 50 m, the vertical resolution and the error separation precision are improved, the Gaussian hypothesis limitation of traditional optimal estimation is broken through, global convergence is ensured through self-adaptive adjustment and step length optimization, and high-precision inversion is achieved.

The application discloses a seepage field cross-scale numerical simulation and feedback analysis method, comprising the following steps: S1, establishing a numerical calculation model for seepage field cross-scale analysis; S2, determining to be inverted seepage parameters and initialization, selecting observation points and constructing an objective function; S3, carrying out seepage cross-scale numerical calculation by using the numerical calculation model to obtain a simulation result of the seepage field; S4, calculating the value of the objective function and a Jacobian matrix according to the simulation result of the seepage field and observation information of an actual seepage field; S5, updating the to-be-inverted seepage parameters by using a Levenberg-Marquardt optimization method; and S6, outputting optimal estimation of the to-be-inverted seepage parameters and the seepage field calculation result when a convergence condition is met. Based on the method, the accuracy of the seepage field cross-scale numerical simulation and feedback analysis result is improved.

The application discloses an engineering measurement error dynamic correction method based on multi-sensor data fusion and relates to the technical field of engineering measurement. By defining three error sources of sensor physical characteristics, environmental interference and human operation as estimable state variables, different characteristics of errors are processed differently: a dynamic model is established to embed the sensor and environmental errors into the state space, real-time compensation based on the model is realized, a gross error identification mechanism based on data-driven statistical characteristics is established to realize real-time diagnosis and processing of human errors, a recursive estimation algorithm is used to perform joint optimal estimation on the unified state vector, and the estimation values of the corrected physical quantity and error states are output synchronously, the scheme significantly improves the measurement accuracy and reliability through dynamic error compensation and optimal fusion, and the stability of the system in a non-ideal environment and online self-diagnosis and health monitoring of the measurement system are enhanced through robust processing of the gross error.

The embodiment of the invention provides a noise filtering method and system for a sensor, and the method comprises the steps: carrying out the modeling of the noise statistical characteristics of a signal of the sensor through employing Gaussian-Gaussian-Gaussian inverse index mixed distribution for a sensor used in an automatic driving scene, obtaining a noise model, and carrying out the calculation of the noise model based on a system state equation and a measurement equation, constructing a joint probability density function of a system state, and forming the joint probability density function into a joint posterior probability density function by adopting a variational Bayesian method; solving an optimal approximate probability density function through a KL divergence between a minimum approximate probability density function and a joint posterior probability density function, and alternately updating q (xk), q (yk), q (pi k) and q (lambda k) by using a fixed point iteration method until iteration convergence; and obtaining the optimal estimation value of the system state xk according to the converged approximate probability density function. The fitting degree of noise description is improved from the source, and precise adaptation and efficient suppression of multi-mode noise of the sensor are achieved.

The application relates to a Bayesian DOA estimation method of an array distortion passive synthetic aperture sonar and belongs to the technical field of signal processing. The method comprises the following steps: performing layered probability modeling on array observation data, the model is used for applying a binary prior distribution to a signal vector to contain sparse induction characteristics; a variational Bayesian method is used to iteratively maximize the lower bound of the marginal likelihood function of array distortion parameters and hidden variables; unknown parameters are iteratively updated according to the estimated posterior distribution; and a spatial spectrum diagram is drawn according to the optimal estimation result, and each DOA is determined according to a peak value. The method solves the problems that a traditional passive synthetic aperture direction finding algorithm cannot estimate the direction of a random source and is sensitive to array distortion.

The invention discloses a thermophysical parameter identification method based on prior distribution regularization, and the method comprises the steps: calculating a covariance matrix of identified thermophysical parameters through an information matrix for a thermal calibration test of a heat-proof material based on temperature observation data of the heat-proof material and the identified thermophysical parameters, the optimal estimation of the identified thermophysical parameters and the covariance matrix are combined to obtain thermophysical parameter distribution of the thermal calibration test, and the thermophysical parameter distribution is used as prior distribution; testing a new batch of heat-proof materials to obtain a group of new temperature observation data, and combining prior distribution with new test data to form a new objective function; the new test data comprises temperature observation data; and obtaining an optimal estimation value of the thermophysical parameter to be identified according to the new objective function. According to the invention, the identification precision and accuracy of the thermophysical parameters are improved.

The invention discloses an inductive sensor linearity improving method and system based on Kalman filtering, belongs to the field of inductive sensors, and solves the problems that a traditional inductive sensor filtering method is limited in processing effect on complex conditions such as environmental noise and device drifting and is difficult to meet application requirements of high precision and high reliability. The method comprises the steps of collecting a state quantity and a measurement quantity of an inductive sensor, and determining an observation equation and a measurement equation of the inductive sensor; adopting a Kalman filtering method to calculate and obtain a Kalman gain; and according to the observation equation, the measurement equation and Kalman gain calculation, an optimal estimation value is obtained, and the linearity of the inductive sensor is improved. The method is suitable for an inductive sensor optimization design and manufacturing scene.

The present application relates to a kind of SF6 gas density relay verification signal anti-jitter processing method and system, comprising: first, the switching quantity signal of relay contact in verification is collected in real time, and the original sequence containing jitter noise is obtained;Then initialize Kalman filtering algorithm, while configuring Snow Ablation Optimizer (Snow Ablation Optimizer, SAO);Then build fitness function containing smoothness, real-time, accuracy index, with its minimization as goal drive Snow Ablation Optimizer optimization, obtain optimal covariance matrix;Optimal parameter is injected into Kalman filtering and resets state, then input original signal point by point, and output anti-jitter signal by recursive optimal estimation;Finally, through threshold value judgment identifies contact action time, records corresponding pressure / density value and completes verification.Compared with prior art, the present application has the advantages of high anti-jitter adaptability and universality, accurate verification result, intelligent verification process and the like.

The invention discloses a fan tower shaking attitude detection method. The method comprises the following steps: acquiring an original three-axis acceleration value and a three-axis angular velocity value of a tower, which are acquired by an inertial measurement unit (IMU) in real time; performing sensor error compensation on the original three-axis acceleration value and the three-axis angular velocity value to obtain a calibrated three-axis acceleration value and a calibrated three-axis angular velocity value under the carrier coordinate system; based on the calibrated three-axis angular velocity value and three-axis acceleration value, data fusion is carried out through an extended Kalman filter (EKF) algorithm, the optimal estimated value of the rotation quaternion representing the attitude of the tower drum is obtained through solving, and an observation noise covariance matrix of the EKF algorithm is dynamically adjusted according to the credibility of the measured value of an accelerometer; according to the optimal estimation value of the rotation quaternion, the shaking attitude angle of the tower drum is obtained through calculation. According to the method, integral drift of the IMU is effectively inhibited, high-precision and high-reliability attitude calculation of quasi-static and dynamic shaking of the tower drum can be realized, and the method is low in cost and easy to deploy.

Provided are a direction of arrival (DOA) estimation method and system for a sparse array based on Vandermonde decomposition reconstruction, relating to the technical field of array signal processing. The method includes: constructing a covariance matrix completion optimization model based on sparse array signals and uniform linear array signals, and performing Vandermonde decomposition by using characteristics of a uniform linear array; introducing a nuclear norm to optimize a rank function in the model, and updating the covariance matrix completion optimization model; and introducing an auxiliary variable to transform the model into a solvable optimization problem, and solving the problem by an alternating direction multiplier method to obtain an optimal estimation value. The DOA is estimated using a root multiple signal classification algorithm.

The invention relates to the technical field of attitude determination of remote sensing satellites, and particularly provides an adaptive Kalman filtering combined attitude determination method and system of a star sensor and a gyro, the method comprises the following steps: constructing a discretization state equation of a spacecraft attitude determination system, the state vector of the system is composed of a quaternion vector part estimation error and a gyro drift error, the state transition matrix is dynamically updated according to the current angular velocity of the gyroscope; establishing a discretization measurement equation of the star sensor; based on the discretization state equation and the discretization measurement equation, adaptive Kalman filtering is adopted to realize optimal estimation of attitude parameters; in the adaptive Kalman filtering process, the measurement noise covariance matrix is dynamically adjusted according to the state estimation residual at the previous moment. According to the self-adaptive Kalman filtering combined attitude determination method for the star sensor and the gyroscope, in the filtering recursion process, the running state of the filter is monitored in real time or quasi-real time, and the statistical property of measured noise is adjusted online, so that the filter can be self-adaptive to a dynamically changing environment.