433 results about "Kalman filtering algorithm" patented technology

The invention provides a combined estimation method for lithium ion battery state of charge, state of health and state of function. The combined estimation method comprises the steps that the state of he---alth of a battery is estimated online: open circuit voltage and internal resistance are identified online by adopting a recursive least square method with a forgetting factor, the state of charge is indirectly acquired according to a pre-established OCV-SOC corresponding relation, and then the size of battery capacity is estimated according to cumulative charge and discharge electric charge between two SOC points; the state of charge of the battery is estimated online: the state of charge of the battery is estimated by adopting the Kalman filter algorithm based on a two-order RC equivalent circuit model, and the battery capacity parameter in the Kalman filter algorithm is updated according to the estimation result of battery capacity; and the state of function of the battery is estimated online: the maximum chargeable and dischargeable current is calculated based on the voltage limit and the current limit of the battery according to internal resistance obtained by online identification, and then the maximum chargeable and dischargeable function can be obtained through further calculation.

The invention discloses a lane line predicating and early warning method suitable for a city road. According to the method, firstly, down sampling is performed on a collected road image, a detection region of interest is set and grayed, filtering enhancement is performed on the region of interest, edge detection is performed through a Canny operator, binarization is performed on the image after edge detection, then the image is mapped on a Hough plane, and lane lines are extracted through improved Hough conversion; then, parameters of the lane line are predicated by the adoption of a classical Kalman filtering algorithm, a region of interest is established, and the lane lines in the region of interest are fitted through a least square method to obtain the optimal predication value; finally, road surface information and current position information of automobiles are combined to establish a lane line departure warning model based on transverse distance, and therefore departure warning is achieved. The recognition method for the lane lines is good in real-time performance and robustness, according to the lane line departure warning model, calculation is easy, and warning is accurate.

A temperature compensation method for denoising a fiber-optic gyroscope on the basis of time series analysis comprises four steps of: step 1, designing an experimental scheme, performing fixed point low and high temperature testing experiment on the fiber-optic gyroscope, and utilizing acquisition software for data acquisition; step 2, performing time series analysis on the zero offset data of the gyroscope, and establishing the mathematical model of the random error of the fiber-optic gyroscope; step 3, adopting a kalman filtering algorithm to filter random noise in the zero offset data of the fiber-optic gyroscope; and step 4, utilizing the data which is de-noised by the kalman filtering to identify the model structure of the temperature shift error of the fiber-optic gyroscope, and calculating the parameters of the identified model. The method establishes the multinomial model of the static temperature shift error of the fiber-optic gyroscope through time series analysis, kalman filtering denoising treatment and identification of the temperature shift error model structure and parameters. The method completely meets the real-time compensation requirement on the project, and has a better practicable value and a wide application prospect in the technical field of aerospace navigation.

Mobile Edge Computing (MEC) has the advantages of high bandwidth and low latency and has attracted extensive attention in the study of next generation of mobile networks by providing service environment and computing power at the edge of mobile networks. The invention discloses a vehicle task unloading and user mobility prediction and edge cache fusion policy in a vehicle networking environment. An MEC-based unloading framework is built. Tasks can be unloaded to an MEC server for vehicle-to-infrastructure (V2I) communications or can be unloaded to an adjacent vehicle for vehicle-to-vehicle (V2V) communications. Considering task backhaul failure caused by link disconnection caused by user mobility, the Kalman filter algorithm is used to predict link connection estimation, and a user can select a stable service node during task request and task collection. Cache decisions are made before the service node first returns a task to a request vehicle. Access latency, traffic load and networkcost can be significantly reduced through content cache.

The invention provides a method and device for estimating a state of charge of a battery. The method comprises the following steps: establishing a multi-parameter fusion mathematical model of a lithium iron phosphate battery; using a fuzzy Kalman filtering algorithm based on an index input membership function to optimize and estimate the state of the charge of the battery, wherein the step of establishing the multi-parameter fusion mathematical model comprises establishing a charge-discharge multiplying power-state of charge sub module, a temperature-state of charge sub module, and a cycle index-state of charge sub module. The method for estimating the state of charge of the battery uses the fuzzy self-adaptive Kalman filtering algorithm based the index input membership function, so that the theoretical value of measurement noise in the Kalman filter is adjusted smoother in a self-adpative manner, thereby improving the matching degree of the system measurement noise, and estimating the state of the charge of the battery more accurately.

The invention discloses a static surveillance video abstraction method based on video moving target detection and tracing, which comprises the following steps: (1) establishing a mixed Gaussian background model detection video moving target; (2) using the kalman filtering algorithm to trace multiple moving targets and store the trace data; (3) dividing the original video into multiple segments according to the trace data; and (4)extracting the key frame of each video segment and adding the motion trail. The method is simple to realize and has better performance.

The invention discloses an indoor simultaneous locating and environment modeling method for an unmanned aerial vehicle. According to the method, flying tracks of the unmanned aerial vehicle can be located, and an indoor three-dimensional environmental model can be drawn fast. An operation platform and an operation method of the unmanned aerial vehicle are constructed, an RGB-D sensor carried by the unmanned aerial vehicle is utilized for collecting data, a visual odometry is adopted for estimating the indoor flying tracks and positions of the unmanned aerial vehicle, and an extended kalman filtering algorithm is adopted for obtaining the more accurate flying tracks and the positions of the aerial vehicle. Furthermore, the data transmission functions of the unmanned aerial vehicle and a ground communication device are utilized for feeding information collected by the sensor on the aerial vehicle back to ground operating personnel in real time, and a three-dimensional data display method of a computer is utilized for calculating and processing the collected data and carrying out indoor three-dimensional environment modeling and displaying.

The invention discloses a dual-wavelength binocular vision seam tracking method. The method comprises the steps including image acquisition, data processing, seam tracking and the like. The invention further discloses a tracking system for implementing the method. The system adopts a near-infrared and structured light dual-wavelength binocular vision sensing system, images of a molten pool and different wavelengths of seam area images with of seams are measured simultaneously and transmitted to a miniature industrial control computer, and the seam positions are measured accurately with a multi-information fusion algorithm and a seam image three-dimensional reconstruction algorithm; the miniature industrial control computer adopts the Kalman filter algorithm to perform optimal estimation on the seam tracking deviation state according to a seam position detection result, a servo driver drives a servo motor to move so as to control a 3-axis motion workbench to generate corresponding motion, a welding torch or a laser head is controlled for deviation correction, and the seams are tracked accurately. The system can eliminate hard light, splashing and electromagnetic interference on a welding site and improve the seam tracking accuracy and reliability.

The invention relates to GPS and MEMS gyroscope and speedometer combination location method and device. While the GPS signal is good, it uses the GPS location signal to locate, rectifies by the MEMS gyroscope and speedometer, synthesize the signal by Kalman filtering algorithm. While the GPS is lost, or the number of the receiving satellite is less than three, it switches to MEMS gyroscope and speedometer to locate. The combination navigation can improve GPS location precision, makes up the unable location defect caused by the GPS is covered.

The invention discloses a power-cell SOC online closed-loop estimation method based on an N-2RC model. In the invention, an electrochemical model and an equivalent circuit model are combined and a novel power cell model is provided. The N-2RC model uses a Nernst electrochemical model to replace an electromotive force portion of a second-order RC equivalent circuit model so that one to one correspondence of a cell electromotive force and SOC can be accurately reflected. Based on the model, a recursive least-square method based on a forgetting factor is used to identify a model parameter, and then an expansion Kalman filtering algorithm is used to realize on-line closed loop estimation of the cell SOC. The electrochemical model can well describe a cell characteristic on an electrochemical aspect, but the structure is complex and the model is not suitable for individually individual usage. And the equivalent circuit model belongs to an external characteristic model and can well express a volt-ampere characteristic relationship of the cell, but can not reflect an internal characteristic of the cell. By using the method in the invention, the above problems are overcome.

The invention discloses combined laser tracking and inertial surveying system for measuring position and gesture of space object composed of inertial surveying subsystem, laser tracking measurement subsystem, and computer control and data collection and process subsystem connected to the above said two systems. The invention is also discloses a dynamic tracking measuring method. Based on requirement of Kalman filtering algorithm, the method builds system equation and observation equation for moving target between fixed connected coordinate system and global coordinate, and edits expanded Kalman filtering algorithm for optimal modifying each error information. So as to calculate out information of position and gesture of target in fixed connected coordinate system related to global coordinate. The invention raises stability and measuring precision, and enriches output information measured.

The invention discloses a small satellite attitude determination system and a method thereof. The system comprises a plurality of attitude measuring units and a central processing unit; the central processing unit is used for collecting measurement data of the attitude measuring units, calculating environment model and selecting corresponding attitude determination algorithm according to the measurement data and the environment model, so as to determine the attitude. The central processing unit consists of a horizon sensor data sampling and processing unit, a solar sensor data sampling and processing unit, a gaussmeter data sampling and processing unit, an environment model calculating unit and an attitude determination selecting unit. The attitude determination selecting unit selects corresponding attitude determination algorithm according to data of the horizon sensor data sampling and processing unit, the solar sensor data sampling and processing unit, the gaussmeter data sampling and processing unit and the environment model calculating unit, so as to determine the attitude. The small satellite attitude determination system has low cost and simple structure; the small satellite attitude determination system also has various attitude determination algorithms, wherein, four fixed attitude determination algorithms and four Kalman filtering algorithms are designed; the algorithms can be effectively integrated and automatically alternated on the satellite to improve reliability of the system.

A local positioning system uses at least one node to track a location of a mobile tag. The system measures flight times of signals communicated between the node and the tag to determine values indicative of the range of the tag from the node. If desired, the values may be filtered in an effort to increase the accuracy of the range estimation. As an example, a Kalman filtering algorithm may be used. Multiple antennas are used at both the node and the tag to provide more accurate range estimates and to determine when the tag is entering a dead zone where signals are blocked or attenuated by obstacles.

The invention discloses a location and environment modeling method of an intelligent movable robot, and the method comprises the steps of firstly forming correction iteration expanded Kalman filtering algorithm and determining a number of iterations, then establishing a movement model and an observation model of the movable robot, initializing the status of the movable robot, calculating a position jacobian matrix, controlling and inputting the jacobian matrix to calculate, observing the jacobian matrix and the like; and finally solving a Kalman gain matrix, updating a status estimation equation and a covariance matrix by resolving Kalman gain matrix, and repeating partial steps. The method is centralized on the expanded Kalman filter algorithm which is widely used in the simultaneous location and environment modeling field of the movable robot, and the algorithm is improved, so that the performance of the algorithm is greatly improved, and the algorithm can better meet the application in the SLAM (simultaneous location and mapping). The method also provides powerful technical support for the autonomous navigation and completion of complicated intelligent tasks of the movable robot in an unknown environment.

The invention discloses a battery SOC online estimation method based on double Kalman filtering algorithm, comprising: S1) obtaining the initial value of the battery SOC; S2) creating a battery equivalent circuit model; obtaining the state equation and the output equation of the battery; S3) using the initial value of the battery SOC as the input state amount and the voltage equation corresponding to the battery equivalent circuit model as the output equation; and utilizing the expanded Kalman filtering algorithm to perform battery SOC estimation; S4) using the battery SOC estimated by the expanded Kalman filtering algorithm as the input state amount and the amper-hour integral method as the output equation; and performing battery SOC estimation through the use of the Kalman filtering algorithm for the estimation value of the battery SOC. The battery SOC online estimation method based on double Kalman filtering algorithm can obtain the SOC estimation value more accurately. Without its excessive reliance on a battery model, the requirement of the method on the current accuracy is also reduced. The Battery SOC online estimation method based on double Kalman filtering algorithm of the invention can find wide applications in the battery identification and estimation field.

The invention discloses a single person positioning navigator based on multi-sensor fusion and a positioning and navigating method. The single person positioning navigator comprises a global satellite positioning system, a barometer, a three-axis gyroscope, a three-axis accelerometer and three-axis geomagnetic sensors. Attitude angle information obtained through the gyroscope, position information obtained through the accelerometer, human body course information provided by the geomagnetic sensors, height information provided by the barometer, stride frequency information worked out by the accelerometer, and position and speed information provided by the global satellite positioning system are input into a kalman filter together, multi-information fusion is carried out through a kalman filter algorithm, and thus positioning and navigating parameters are output. Even when the global satellite positioning system cannot stably receive information due to shielding, electromagnetic interference and other factors, the single person positioning navigator based on multi-sensor fusion and the positioning and navigating method can achieve effective positioning and navigating and are suitable for various environments such as inside rooms or inside tunnels. By means of the single person positioning navigator based on multi-sensor fusion and the positioning and navigating method, positioning errors accumulated along with time can be reduced, and accurate individual 3D positioning can be achieved.

The invention relates to a multi-sensor combined navigation system for aviation, and the system comprises an MSINS unit, a GPS unit, a magnetic compass unit, a liquid crystal display unit, a data transmission interface unit, a combined navigation computer, a data storage unit and a power supply device. The MSINS unit is used for acquiring signals of an accelerometer and a gyroscope, carrying out filtration and amplification and sending the signals to a DSP for carrying out algorithm processing; a GPS module is used for outputting longitude, latitude, altitude and velocity values of X, Y and Z axes based on a geocentric coordinate system measured by the GPS; the magnetic compass unit is used for outputting three-dimensional attitude information measured by a three-axis magnetic compass; the combined navigation computer is used for receiving signals outputted by an inertial measurement unit, the GPS module and the magnetic compass, applying the federated Kalman filtering algorithm to carry out fusion treatment on data and obtaining combined navigation data; and the data storage unit is used for saving original data and result data. The system has the advantages of small volume, low cost, high reliability and high precision.

The invention discloses a method for estimating lithium battery SOC based on self-adaptability fuzzy Kalman filter. For different lithium batteries, after Kalman filter discrete state model is determined, the SOC is estimated by the utilization of the method of self-adaptability fuzzy Kalman filter, wherein the residual variance matching degree and residual error mean of terminal voltage in a lithium battery equivalent model serve as input of a fuzzy control system, so that adjustment factors of a system noise variance and a measurement noise variance are obtained, and then the two variances are adjusted; the adjusted system noise variance and measurement noise variance are substituted into a Kalman filter algorithm to estimate SOC values at all moments. With the method, the power lithium battery SOC can be accurately estimated, the problems that according to an existing estimation method, the requirement for on-line estimation can not be satisfied, has large accumulated errors, diverges and is prone to being affected by noise can be solved, and the estimation accuracy is high.

The invention discloses a power lithium battery SOC estimation method based on the self-adaptive Kalman filtering method. The power lithium battery SOC estimation method comprises the following steps:at first, according to the dynamic characteristics of a lithium ion battery, establishing a dual-polarization equivalent circuit model of the battery; then, obtaining data through testing the performance of the composite pulse power, identifying the characteristic parameter of the model, and adopting the least squares fit to obtain a relation curve of the open-circuit voltage and SOC; based on the relation curve of the open-circuit voltage and SOC and the discrete equation of a DP model, establishing a state equation and an observation equation, and substituting the state equation and the observation equation into the EFK algorithm to obtain a system matrix; and finally, adopting the modified self-adaptive extended Kalman filtering algorithm to estimate the battery SOC. With adoption of the power lithium battery SOC estimation method, the problems that the filtering results diffuse and the operation is not stable when the traditional self-adaptive Kalman filtering method or the EFK algorithm is adopted for SOC estimation are effectively solved, and the speed that the SOC estimated value is convergent to the truth value is increased.

The invention belongs to the field of electromobile power battery management, and relates to a unite online estimation method of electromobile power battery system SOC and SOH. The method mainly includes the following steps that firstly, experimental data are acquired, a battery model is established and initial valves of model parameters are recognized, and coefficient initial valves A0, B0, C0 and D0 of a space equation are acquired; secondly, two extended Kalman filtering (EKF) rings respectively estimate the battery set SOC and internal resistance R0, and estimation results of the SOC and R0 can be modified with each other; and thirdly, the estimation results of the SOC and R0 in the second step are input in to a BCRLS algorithm to output the recognized model parameters R0, R1, and C1,and space equation coefficients Ak, Bk, Ck, and Dk are updated to estimate the SOC and the SOH at the next moment. The method fuses a dual Kalman filtering algorithm and the BCRLS algorithm, the problem that an algorithm no longer has unbiasedness due to uncertain noises is solved effectively, the accuracy of a battery set model is improved, the dual Kalman filtering algorithm effectively avoids the impact of an online SOC valve on battery SOH estimation, and the estimation accuracy and robustness of the SOH are improved.

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 pedestrian autonomous navigation calculation algorithm based on an MEMS-IMU. Two IMU systems are simultaneously and fixedly connected with the two feet of the user of a pedestrian navigation system; the two systems respectively perform a strapdown inertial navigation calculation algorithm and a zero-speed correcting algorithm on the basis of Kalman filtering, and then the positioning information of the two feet is fused; when the calculation distance between two feet exceeds the largest step size gamma between the two feet, inequality constraint is performed on navigation results of two IMUs by adopting a state constraint Kalman filtering algorithm, so that vague human physiological property problem is converted into a rigid mathematical problem, the optimal estimation of navigation results is achieved, and pedestrian navigational positioning function with higher accuracy is achieved.

The invention provides a strap-down inertial navigation system/visual odometer integrated navigation method which comprises the following steps: mounting a binocular visual odometer and a fiber-opticgyroscope inertial navigation system on a transporter and collecting data of all sensors; extracting features in an image sequence with an FAST method, completing feature matching with a feature matching method based on random sample consensus and calculating movement information of the transporter; establishing a nonlinear state equation and a measurement equation of a strap-down inertial navigation system/visual odometer integrated navigation system; and completing time update and measurement update of the strap-down inertial navigation system/visual odometer integrated navigation system with a volume Kalman filter of a nonlinear filter, and estimating the state of the system, so as to realize the navigation and location of the strap-down inertial navigation system/visual odometer integrated navigation system. According to the strap-down inertial navigation system/visual odometer integrated navigation method, a feature matching algorithm is optimized, and a nonlinear volume Kalman filter algorithm is utilized, so that the location accuracy and the robustness of the integrated navigation system are improved.