35 results about "Unscented particle filter" patented technology

There are a sort of predictive filtering and the self-demarcation of the UPF spaceflight, it relates to the spaceflight airmanship field. It applies to the self-demarcation of the spaceflight peg-top, especially it relates to a sort of spaceflight self-demarcation method of the inertia / starlight which bases the predictive filtering and the UPF (Unscented Particle Filter) information coalescence, thereby it applies to the navigation determining gesture of the spaceflight. First it establishes the self-demarcation state equations of the spaceflight, and then it makes the gesture information which is observed by the star sensing device to become the measuring equations, finally it adopts the self-demarcation arithmetic which bases the predictive filtering and the UPF spaceflight to estimate and amend the drift error of the peg-top, thereby it obtain the high exact gesture of the spaceflight.

The invention relates to a simultaneous localization and mapping method based on distributed edge unscented particle filter. First, a coordinate system is built and an environmental map is initialized; then subfilters are built for each landmark point with successful matching respectively; next, based on a robot motion model, a particle swarm is generated in each subfilter respectively, and the state vector and the variance of each particle are obtained; noise is introduced, particle state vectors after extension are calculated by utilization of unscented transformation, the particles after extension are updated and the particle swarms are optimized; then particle weights are calculated and normalization is carried out, and aggregated data of each subfilter are subjected to statistics and the data are sent to a master filter; next, global estimation and variance are calculated; then the effective sampling draw scale and sampling threshold of each subfiter are determined, the subfilters with severe particle degeneracy are subjected to resampling; then the state vectors and the variances of the robot are output, and stored in a map. Finally, landmark point states are updated by utilization of kalman filtering algorithm until the robot is no longer running.

The invention discloses a high accuracy combined affirming gesture method based on genetic optimum REQUEST and GUPF, which comprises the following steps: proceeding compensation top exporting data and attitude resolve for inertia measurement information; getting real time carrier gesture information; measuring information through astronomical; combining top outputting data after compensating; quick-getting high accuracy astronomical attitude information with special interval based on genetic optimum REQUEST method; fusing the astronomical attitude information and the carrier attitude information with Genetic Unscented Particle Filter(GUPF) algorithm; solving high accuracy carrier attitude information; estimating the shift of the top; reacting and correcting carrier attitude; compensating the shift compensation of top; finishing long time and high accuracy affirming gesture for spacecraft.

The invention provides an MCAUPF based target tracking method. An MCAUPF is used to complete state estimation in the target tracking process. In the target tracking process, target tracking equation and measurement equations are reconstructed into a nonlinear recursion model, in the framework of unscented particle filtering, a significance probability density function needed by filtering particlesgenerated by maximum correntropy unscented Kalman filtering is used, a Kullback-Leibler distance resampling method is used to resample the generated particles, the state of the tracking target is estimated according to a UPF algorithm flow, and the target is tracked in real time. The MCAUPF method is applied to target tracking in which outliers occur in noise measurement, the precision is highercompared with that of present PF, improved PF and robust filtering, and the computing complexity is lower than that of a present improved particle filter algorithm.

The invention discloses an autonomous navigation method of an AUV (Autonomous Underwater Vehicle) based on a FastSLAM (Simultaneous Localization and Mapping) algorithm. The autonomous navigation method comprises the steps that 1) the AUV acquires initial pose and position information through the GPS and a navigation sensor on the water surface; 2) predicting the pose and position and an environmental road sign of the AUV by adopting unscented particle filtering according to latest control variables inputted into the AUV and observation variables of the sensor; 3) generating a proposal distribution function for parameter adaptive adjustment by adopting fading adaptive unscented particle filtering, and sampling in the proposal distribution function; 4) associating the latest observation environment information according to each particle, and updating estimation for each characteristic by adopting unscented Kalman filtering; 5) performing resampling on a particle set by adopting an adaptive partial system resampling method; and 6) performing AUV positioning and map building. The autonomous navigation method can improve the particle sampling efficiency of the Unscented FastSLAM algorithm and reduce the degradation degree of the particles through improving the proposal distribution function and the resampling process of the Unscented FastSLAM algorithm, thereby enabling the consistency of AUV pose and position estimation and the accuracy of autonomous navigation to be greatly improved.

The invention discloses an integrated attitude determination method based on an ant colony unscented particle filter algorithm, which relates to an inertial / astronomical integrated attitude determination method. The method comprises the following steps of: compensating gyro output data by using inertial measurement information and acquiring carrier attitude information through attitude calculation; acquiring required astronomical attitude information by using astronomical measurement information through a deterministic algorithm; fusing the astronomical attitude information with the carrier attitude information by using an ant colony unscented particle filter algorithm, solving nonlinear and noise non-Gaussian problems of a system, solving high-accuracy carrier attitude information, estimating gyro drift, and feeding back and correcting carrier attitude and compensating the gyro drift; and finally realizing on-line correction of eliminating random errors of a gyro of an inertial / astronomical integrated navigation system in real time based on the astronomical measurement information, and finishing long-term and high-accuracy combined attitude determination of a spacecraft.

The invention discloses a method for estimating dynamic states of power generators on the basis of unscented particle filtering theories. The method includes utilizing fourth-order dynamic equations of the power generators as state equations for estimating the dynamic states of the power generators, simulating PMU (phasor measurement units) by the aid of power system analysis software to acquire measurement data of power angles, angular speeds and the like of the power generators, and creating measurement equations for the power generators; acquiring static estimation values at state estimation initial moments, utilizing the static estimation values as initial values for the power generators at dynamic state start moments, generating initial particles adjacent to the initial values, carrying out tracking filtering on state variables of the power angles, the angular speeds and the like of the power generators by the aid of unscented particle filtering algorithms to ultimately obtain estimation values of the state variables of the power generators. The method has the advantages that the quantity demands on the particles can be lowered, and the filtering accuracy and the computational efficiency of the method are superior to the filtering accuracy and the computational efficiency of the traditional particle filtering processes; the dispersibility of the particles is improved by the aid of the method, and accordingly the robustness of the method is superior to the robustness of the traditional particle filtering processes and unscented Kalman filtering processes.

The invention relates to a strap-down inertial navigation system large azimuth misalignment angle initial alignment method based on MRUPF (Multi-resolution Unscented Particle Filter), comprising the following steps of: firstly, establishing a state space model for the initial alignment of a strap-down inertial navigation system static substrate under the large azimuth misalignment angle condition to carry out filtering initialization; then carrying out the state estimation of the initial alignment by utilizing a UPF filtering algorithm; selecting particles and particle weights by utilizing a multi-resolution method at a moment to reduce the number of the particles; and continuing to carry out the state estimation of the initial alignment by utilizing the UPF filtering method by taking the selected particle set and the particle weights as initial particle sets and the weights to obtain a misalignment angle estimated value. In the invention, the UPF particle number is reduced through the multi-resolution method, thereby reducing the calculated amount, and the real-time performance of the initial alignment of the strap-down inertial navigation system under a large azimuth misalignment angle is improved while the initial alignment accuracy is ensured. The invention is suitable for the initial alignment of the strap-down inertial navigation system.

The invention discloses a full-drive AUV recovery control system and an independent recovery method, and provides a hardware structure for a full-drive AUV recovery docking control system, an independent recovery method for keeping linear tracking and hovering calibration by using dynamic positioning, and a dynamic positioning state estimation method based on adaptive unscented particle filter toimprove the stability of AUV recovery docking. The adopted adaptive unscented particle filter algorithm is used for estimating the statistical characteristics of unknown noise by Saga-Husa, and the advantages of the particle filter and the UKF algorithm are complementary by using particle distribution in combination with approximate state posterior distribution. The tracking filter effect is better under the influence of external disturbance, physical characteristics of acceleration and human manipulation, the stability of dynamic positioning is effectively improved, and the success rate of independent recovery is improved.

The invention provides an unscented particle filtering method based on a particle swarm optimization algorithm, which comprises the steps of: 1, initial time: obtaining a group of initial particles from an initial distribution p(x0), setting an initial average and variance of the group of initial particles; 2, sampling sequential importance; 3, updating weight; 4, obtaining an normalized weight; 5, sampling; 6, updating state; and 7, solving a globally optimum solution G(t) at the current time. The invention ensures that a particle swarm more trends to a high likelihood region before the weight is updated through a particle swarm optimization process, thereby solving the problem of sample depletion to a certain degree. The optimization process ensures particles far away from real state trends to an area with high occurrence probability of the real state, thereby improving the action effect of each particle. Compared with other intelligent optimization algorithms, the particle swarm optimization algorithm has the advantages of easy implementation and no adjustment on various parameters, lowers the particle number required by accurate estimation, and improves the computing efficiency of filtering.

The invention relates to an UPF automatic astrogation based on the star angular distance. It starts with precise modeling of the UPF track dynamics, using the star angular distance as the measuring unit, and optimizing the estimation based on the UPF (unscented particle filter) for the guide parameter. It applies to UPF guide location on the track transfer for the guide parameter determination.

The invention provides a particle filtering method based on spherical simplex unscented Kalman filter. The particle filtering method comprises the steps of: initializing a particle and a weight value thereof; generating a particle through importance sampling; updating and normalizing the particle weight value; sampling again; outputting a result; and entering a next time step. The invention mainly improves an importance sampling step and obtains importance probability density by adopting a SSUKF (Spherical Simplex Unscented Kalman Filter) algorithm based on SSUT (Spherical Simplex Unscented Transformation). Compared with PF (Particle Filter), EKPF (Extended Kalman Particle Filter) and standard unscented particle filter, SSUPF (Spherical Simplex Unscented Particle Filter) can acquire the precision equivalent to UPF (Unscented Particle Filter). On the other hand, because the SSUT adopts sampling points, i.e. sigma points, which are distributed in a spherical way, the quantity of the sampling points is far less than the UT (Unscented Transformation), and the advantage on the aspect of computing efficiency is gradually obvious in a high dimensional system.

The invention discloses a vehicle state estimation method based on adaptive volume particle filter (ACPF), which comprises the following steps: firstly, constructing a high-dimensional nonlinear eight-degree-of-freedom vehicle dynamic model based on an unsteady state dynamic tire model; secondly, updating an importance density function of a basic particle filtering algorithm by utilizing an adaptive volume Kalman filtering algorithm so as to complete the design of the adaptive volume particle filtering algorithm; achieving high-precision online observation of key state variables such as roll angle and side slip angle of a vehicle by using vehicle-mounted sensor information and an ACPF algorithm; finally, building a Simulink-Carsim joint simulation platform for algorithm verification. The result shows that the algorithm state estimation precision is higher than that of a traditional unscented particle filter (UPF) algorithm, and the algorithm operation efficiency is higher than that ofthe UPF algorithm.

The invention relates to the field of life prediction of mechanical and electrical equipment, and discloses a method of predicting the remaining life of equipment based on improved unscented particle filtering, in order to solve the problem that there is a loss of particle diversity for unscented particle filtering in the traditional method of predicting the remaining life of equipment, and to improve the accuracy of equipment life prediction. Unscented Kalman filtering is used as a recommended density distribution function of particle filtering to reduce particle degradation, and a linear optimization re-sampling algorithm is used to optimize the re-sampling part of particle filtering. For the selection of the step length coefficient K of linear optimization re-sampling, an adjustment factor Kb is created, the value of the step length coefficient K is determined adaptively using a fuzzy reasoning system, and finally, the remaining useful life of equipment is predicted. The method of the invention is suitable for residual useful life prediction of mechanical and electrical equipment.

The invention relates to a railway vehicle suspension system parameter estimation method based on an improved particle filtering algorithm. The method comprises the following steps: (1), a kinetic model of a railway vehicle is built in many-body dynamics software; (2), motion information acquisition equipment is arranged in corresponding positions of a vehicle body and a bogie of the kinetic model, and simulated motion information of the vehicle is acquired; (3), the simulated observed value of the motion information of the vehicle is acquired; (4), vertical and horizontal kinetic models of a railway vehicle system are built, and vertical and horizontal dynamic space models of the railway vehicle system are further built; and (5), according to the simulated observed value obtained, through the combination with the improved particle filtering algorithm, the system parameter and the system unknown parameter matrix are estimated at the same time. Compared with the prior art, a uniform resampling strategy is introduced, so that the tradition method needing to rely on the statistical result of the mass state monitoring data is broken through, and the problem that the change of the parameters of the suspension system cannot be monitored in a real-time manner due to unscented particle filter is solved.

The invention provides an oilfield mechanical oil extraction parameter modeling method based on an unscented particle filtering neural network. The method comprises the steps of determining efficiency affecting factors and performance variables in the oil extraction process of oilfield machinery; performing dimension reduction processing on load variables in a sample so as to reconstruct a new sample, and normalizing the new sample; building a neural network model based on the normalized new sample; estimating an optimal state variable of the neural network model by using an UPFNN algorithm, and building an oilfield mechanical oil extraction process model by using the optimal state variable; inputting X^ in the normalized new sample into the oilfield mechanical oil extraction process model to acquire a prediction result, comparing the prediction result with Y^ in the normalized new sample, wherein the oilfield mechanical oil extraction process model is effective if the comparison result is less than a preset error value, otherwise repeating all of the above steps until the comparison result is less than the preset error value. According to the invention, working conditions of the oilfield machinery are predicted through mining production laws of the oilfield machinery, and a basic model is provided for mining optimal production operations of the oilfield machinery.

The invention discloses a human body abnormal behavior detection and recognition system and method, and belongs to the technical field of monitoring camera shooting. The mixing method is based on continuous adaptive mean shift (CAMS). A correction background weight histogram CBWH and an unscented particle filter UPF technology are introduced to process interference of shielded and similar color objects. A detection mode based on sparse representation is adopted to detect and identify abnormal behaviors of a target object from multiple scenes, the performance of the proposed method is evaluatedby using mean square error statistics, and simulation verification is carried out on a public data set UMN at the same time. Experimental results show that the method provided by the invention can accurately detect and identify the target object under the condition that obstacles are shielded or other objects with similar colors exist in different scenes. In addition, the technology may further improve the tracking performance of the target object in multiple cameras in a complex scene.

The invention discloses a health state and residual life prediction method of a multi-type lithium ion battery pack management system, and aims to solve the problems that under the condition that multiple types of lithium ion batteries are used in a mixed mode, a traditional battery management system cannot achieve effective management, and the prediction starting point of a traditional predictionmodel is relatively backward. According to the method, wavelet decomposition is adopted for lithium ion batteries of the same type, capacity degradation historical data are divided into a high-frequency fluctuation part and a low-frequency trend part, and the two parts of data serve as input data to train wavelet neural networks with residual layers corresponding to the two parts of data; real-time low-frequency trend data is substituted into a residual wavelet network and unscented particle filter combined model to obtain a long-term residual life prediction result, wherein the result provides a basis for the later battery replacement sequence of the system; and the residual life prediction result and a short-term prediction value obtained by using a wavelet neural network model with a residual layer in a real-time high-frequency fluctuation part are superposed through the same number of cycles to obtain a real-time health state prediction value for lithium ion battery health state balance management.

There are a sort of predictive filtering and the self-demarcation of the UPF spaceflight, it relates to the spaceflight airmanship field. It applies to the self-demarcation of the spaceflight peg-top, especially it relates to a sort of spaceflight self-demarcation method of the inertia / starlight which bases the predictive filtering and the UPF (Unscented Particle Filter) information coalescence, thereby it applies to the navigation determining gesture of the spaceflight. First it establishes the self-demarcation state equations of the spaceflight, and then it makes the gesture information which is observed by the star sensing device to become the measuring equations, finally it adopts the self-demarcation arithmetic which bases the predictive filtering and the UPF spaceflight to estimate and amend the drift error of the peg-top, thereby it obtain the high exact gesture of the spaceflight.

The invention relates to a joint motion estimation method based on a myoelectricity and myotone model and unscented particle filtering. The method comprises the following steps: firstly, acquiring surface myoelectricity and muscle sound signals of biceps brachii muscle, triceps brachii muscle, radial brachii muscle, trapezius muscle, adductor muscle, anterior deltoid muscle, lateral deltoid muscleand pectoralis major muscle of an upper limb shoulder joint and an elbow joint of a human body in a synchronous continuous motion state, and respectively performing band-pass filtering processing; then, extracting Wilson amplitude and fuzzy entropy features of the surface myoelectricity and myotone signals; combining the physiological muscle model and joint kinematics through parameter substitution and simplification to form a joint motion model, and forming a measurement equation by using the extracted features to serve as feedback of the joint motion model to obtain a myoelectricity myotonestate space model; and finally, estimating the synchronous continuous motion of the shoulder joint and the elbow joint through an unscented particle filter algorithm. Compared with a traditional multi-joint synchronous continuous motion estimation method, the method has the advantage that the prediction precision and the real-time performance are obviously improved.

The invention discloses a high-precision indoor positioning method based on WiFi-PDR fusion. The method comprises the following steps: acquiring environment beacon data; matching a pre-established position-received signal strength fingerprint database to obtain initial positions of a plurality of targets; according to the reference path loss coefficient and an ITU model, calculating to obtain a weight coefficient of each AP, and calculating a coarse position of the target; collecting a course angle and an acceleration value of the target; smoothing the original acceleration value data to reduce noise interference; dynamically setting state conversion parameters according to noise reduction data collected in real time, and calculating the step number of a target; according to a non-linear step length estimation method, calculating the single step length of the target; and performing fusion calculation through an adaptive unscented particle filtering algorithm to obtain accurate motion state information of the target. According to the method, the WiFi fingerprint positioning precision is improved, the error accumulation effect of the PDR method is reduced, the WiFi-PDR fusion positioning method is optimized, the positioning continuity and stability are improved, and indoor positioning is more accurate and effective.

The invention relates to an UPF automatic astrogation based on the star angular distance. It starts with precise modeling of the UPF track dynamics, using the star angular distance as the measuring unit, and optimizing the estimation based on the UPF (unscented particle filter) for the guide parameter. It applies to UPF guide location on the track transfer for the guide parameter determination.

The invention discloses a battery remaining service life prediction method based on maximum expectation-unscented particle filtering, and relates to a battery remaining service life prediction method. The objective of the invention is to solve the problem that the sudden increase of the capacity of the existing battery causes a great error in the prediction of the remaining service life of the battery. The method comprises the following steps: 1, extracting battery capacity data in the kth working process; 2, constructing a dynamic battery degradation model based on unscented particle filtering; 3, adaptively estimating process noise and measurement noise in the battery degradation model by adopting an expectation maximization algorithm; the process noise and the measurement noise are used in the (k + 1) th working process, and a dynamic battery degradation model based on unscented particle filtering is constructed; 4, judging whether a capacity regeneration phenomenon occurs in the kth working process or not; 5, solving a confidence interval between the remaining service life of the battery and the battery capacity; the method is used in the field of residual service life prediction of the battery.

The invention discloses an unscented particle filtering method for SOC estimation of a lithium ion battery pack of a large unmanned aerial vehicle. The unscented particle filtering method comprises the following steps: S01, constructing a battery model according to a relationship between influence factors of the SOC of the lithium ion battery pack and nonlinear working characteristics of the lithium ion battery pack due to internal parameter coupling; s02, collecting and integrating various data of the battery pack; s03, updating a particle set in particle filtering algorithm sampling according to the integrated data by using a mean value and a variance obtained by an unscented Kalman filtering algorithm; and S04, predicting the working characteristics of the lithium ion battery pack according to the calculation in the step S03. The invention has the advantages that unscented Kalman filtering is adopted, a good filtering effect is achieved, errors caused by a linearization process are ingeniously avoided by adopting probability density fitting of system state variables, estimation precision is further improved, and system robustness is better.

The invention relates to a Gaussian mixture unscented particle filter algorithm employing adaptive resampling. The common single-Gaussian particle filter algorithm is optimized in two aspects, namely importance density function and importance resampling. Gaussian mixture unscented transformation is used as the importance density function for particle filtering, and system state can be estimated accurately. On the basis of traditional residual resampling, the adaptive residual resampling simple and effective is provided in order that particle degeneracy and particle impoverishment are relieved. An uncertainty model, random surfer model, is used as a state model is used in order to estimate the performance of the algorithm. Simulation results show that the algorithm is superior to the common single-Gaussian particle filter algorithm in terms of tracking precision, state estimation and particle set diversity.

The invention discloses an integrated attitude determination method based on an ant colony unscented particle filter algorithm, which relates to an inertial / astronomical integrated attitude determination method. The method comprises the following steps of: compensating gyro output data by using inertial measurement information and acquiring carrier attitude information through attitude calculation; acquiring required astronomical attitude information by using astronomical measurement information through a deterministic algorithm; fusing the astronomical attitude information with the carrier attitude information by using an ant colony unscented particle filter algorithm, solving nonlinear and noise non-Gaussian problems of a system, solving high-accuracy carrier attitude information, estimating gyro drift, and feeding back and correcting carrier attitude and compensating the gyro drift; and finally realizing on-line correction of eliminating random errors of a gyro of an inertial / astronomical integrated navigation system in real time based on the astronomical measurement information, and finishing long-term and high-accuracy combined attitude determination of a spacecraft.

The invention discloses a full-drive AUV recovery control system and an autonomous recovery method, provides a hardware structure for a full-drive AUV recovery docking control system, an autonomous recovery method that uses dynamic positioning to maintain linear tracking and hovering calibration, and A dynamic positioning state estimation method based on adaptive unscented particle filter that can improve the stability of AUV recovery and docking. The adopted adaptive unscented particle filter algorithm uses Saga-Husa to estimate the statistical characteristics of unknown noise, and uses the particle distribution to jointly approximate the state posterior distribution, which complements the advantages of particle filter and UKF algorithm. In the case of external interference, physical characteristics of acceleration and human manipulation, the tracking filtering effect is better, which effectively improves the stability of dynamic positioning and the success rate of autonomous recovery.

The invention provides an oilfield mechanical oil extraction parameter modeling method based on an unscented particle filtering neural network. The method comprises the steps of determining efficiency affecting factors and performance variables in the oil extraction process of oilfield machinery; performing dimension reduction processing on load variables in a sample so as to reconstruct a new sample, and normalizing the new sample; building a neural network model based on the normalized new sample; estimating an optimal state variable of the neural network model by using an UPFNN algorithm, and building an oilfield mechanical oil extraction process model by using the optimal state variable; inputting X^ in the normalized new sample into the oilfield mechanical oil extraction process model to acquire a prediction result, comparing the prediction result with Y^ in the normalized new sample, wherein the oilfield mechanical oil extraction process model is effective if the comparison result is less than a preset error value, otherwise repeating all of the above steps until the comparison result is less than the preset error value. According to the invention, working conditions of the oilfield machinery are predicted through mining production laws of the oilfield machinery, and a basic model is provided for mining optimal production operations of the oilfield machinery.